Publications | Dylan Chiang

Promoting Integration through Competition: Research on the Model and Path of Constructing Cross-Strait Youth AI Innovation Competition

Sun, 12 Oct 2025 00:00:00 +0000

1. Origin of the problem and research significance

Promoting the integration of youth from both sides of the Taiwan Strait is a key link in the historical process of realizing the great rejuvenation of the Chinese nation. For a long time, the mainland has always promoted cross-Strait youth exchanges with the greatest sincerity, and has played a positive role in enhancing mutual understanding between cross-Strait youths through traditional modes such as visits, study tours, and cultural festivals. However, with the changing times and profound changes in the information environment, traditional communication models are gradually facing new challenges. For “Generation Z” Taiwanese youth who have grown up with the Internet, short videos and artificial intelligence, the appeal of one-way visits or lecture-style communication is declining. They are more eager for experiences that enable in-depth participation, interactive collaboration and the realization of personal value. At the same time, Taiwan’s complex public opinion environment may interfere with the effectiveness of traditional exchanges, causing some Taiwan youth’s understanding of the mainland to remain at the level of fragmented and one-sided information, making it difficult to form a comprehensive and objective understanding. In this context, how to build a new communication paradigm that is more in line with the characteristics of contemporary youth, can better stimulate their inner driving force, and can better overcome information barriers has become an urgent issue in the current cross-strait youth exchange work. Against this background, the cutting-edge scientific and technological field represented by artificial intelligence (AI) is becoming an important breakthrough to solve this dilemma.

This study aims to systematically study the models and paths of cross-strait youth AI competitions, and explore how to promote the in-depth integration of cross-strait youths through event design, which has theoretical and practical significance. In a theoretical sense, it helps to enrich the theoretical system of cross-strait youth exchanges and identity. Existing research mostly focuses on the evaluation of the effects of traditional communication models, while paying less attention to new communication paradigms driven by AI technology. This study will combine the issue setting theory of communication, social identity theory and contact theory of social psychology to deeply analyze the internal mechanism of AI competition in promoting youth integration, and provide a new theoretical perspective for understanding cross-strait youth interaction in the new era. In a practical sense, this research aims to provide an operational reference framework for government departments at all levels, universities and technology companies to design and organize cross-strait youth AI competitions. By systematically sorting out the experience and dilemmas of existing events, this study will propose a three-in-one event model of “issue guidance-collaboration and co-creation-value empowerment” in order to help upgrade cross-strait AI events from a mere “technical interaction” to an effective “integrated identity” platform, effectively improve the quality and effectiveness of youth exchanges with Taiwan, and provide solid practical support for promoting the common development of cross-strait youth and ultimately achieving spiritual harmony.

2. Development status and practical difficulties of cross-strait youth AI competition

Judging from current practice, the development of cross-strait youth AI competitions shows obvious characteristics of “technical empowerment + scenario expansion”. Initial attempts were mostly focused on competitive competitions with technology as the core. For example, the 3rd Cross-Strait Artificial Intelligence-Industrial Robot Competition held in Fuzhou in 2025 attracted the participation of 60 cross-strait teams, including 11 Taiwanese teams and 15 Fujian and Taiwan mixed teams. By setting up specific technology tracks such as drones and intelligent handling, and combining it with an “online + offline” hybrid model, this type of event has successfully provided young people from both sides of the Taiwan Strait with a primary interactive platform based on professional skills.

On this basis, competitions focusing on innovation and entrepreneurship further connect technology with industrial needs, providing Taiwanese youth with more direct opportunities to implement. The “Cross-Strait Youth Innovation and Entrepreneurship Competition” held in Changzhou in 2025 is a typical example. The competition has high-end equipment, electronic information and other tracks, and a special “Taipei Youth Service Station” has been set up to provide policy consultation, mentor matching and investment and financing services to Taiwanese contestants. Finally, through project signing and park settlement, “competition promotes production”. The significance of this type of competition is to transform “integrated development” from an abstract concept into concrete career opportunities, thus effectively enhancing the motivation of Taiwanese youth to participate.

Although the Cross-Strait Youth AI Competition has made the above progress, there are still some deep difficulties in achieving the core goal of “promoting integration through competition”, which makes the integration effect easy to remain at a shallow level of interaction and difficult to transform into deep recognition. Including the limitations of topic setting, the de-institutionalization of collaboration mechanisms, and the break of the post-game value transformation chain, etc. Overall, the development of the Cross-Strait Youth AI Competition has gone through the initial stage of using technology as a medium to attract participation. However, to achieve its ultimate goal of promoting “spiritual fit”, it must also cross the key gap from “technical interaction” to “integrated identity”.

3. Conceptual model of “promoting financing through competition”: issue guidance, collaborative co-creation and value empowerment

In response to the difficulties that cross-strait youth AI competitions face in practice such as limited topics, loose collaboration, and broken value chains analyzed previously, this study proposes a set of systematic solutions with the core goal of “promoting integration and identity”—a three-in-one “promoting integration through competition” conceptual model of “issue guidance-collaboration and co-creation-value empowerment”.

Issue guidance is the logical starting point and soul of this model. Its core lies in sublimating the competition from a mere “technical problem solving” to a “mission practice” carrying a common vision. According to the issue setting theory, the issues of the event directly shape the cognitive framework and value orientation of the participants. The design of the topic should follow three basic principles: first, practical relevance; second, commonality of values; and third, technological cutting-edgeness. Through the topics designed in this way, the competition is no longer just a technical competition, but has become a platform for thought and action for young people on both sides of the Taiwan Strait to work together to cope with common challenges and create a better future. Under the guidance of grand issues, collaborative co-creation has become a key practical link to internalize “common mission” into “collective identity”. Specific strategies may include: first, implementing mandatory mixed formations; second, designing a structured collaboration process; third, providing an integrated AI collaboration platform that integrates cloud computing power, shared data sets and collaborative programming tools from platforms such as Baidu’s “Fei Paddle” or Alibaba’s “Tianchi” to clear technical obstacles for cross-regional collaboration.

Ultimately, Value Empowerment constitutes the closed loop and long-term guarantee of this model, aiming to transform short-term participation in the event into long-term value for the personal development of Taiwanese youth, thereby achieving a sustainable integration effect. According to expectancy theory, the strength of an individual’s motivation to participate in an activity depends on his or her assessment of the expected rewards that the behavior can bring. Therefore, a complete post-game value conversion chain must be built to make the participation experience truly “value for money.” This chain should include three levels: first, the connection of career development channels; second, the incubation support of innovative projects; and finally, the construction and maintenance of long-term communities, that is, all contestants will be included in the “Cross-Strait Youth AI Talent Pool”, and through regular online sharing, offline visits and project cooperation, a communication network for continuous interaction and common growth will be formed.

4. Specific case: “Straits Youth AI Integration Innovation Competition” program design

In order to transform the aforementioned conceptual model of “issue guidance-collaborative co-creation-value empowerment” into an operational practical path, this study designed a specific case called the “Strait Youth AI Integration Innovation Competition” (hereinafter referred to as the “Contest”).

The core positioning of the competition is “a flagship AI innovation event serving the integrated development of both sides of the Taiwan Strait”, and its annual theme is set as “AI Empowers Collaborative Innovation of Straits Ports and Industrial Chains”. Participants are open to young people aged 18 to 45 on both sides of the Taiwan Strait, including college students, young engineers and start-up enterprise teams, aiming to attract the most innovative groups from both sides of the Taiwan Strait. In order to ensure the professionalism and authority of the event, the competition is planned to be guided by the Taiwan Affairs Office of the State Council, the Ministry of Science and Technology and other national ministries and commissions, and jointly organized by the Fujian Provincial People’s Government and relevant local governments (such as Xiamen, Fuzhou, and Quanzhou). Top universities on both sides of the Taiwan Strait (such as Tsinghua University, Xiamen University, and National Taiwan University) and mainland China’s leading technology companies (such as Zhipu Technology, Volcano Engine, Magic Square Quantification) will be invited to deeply participate in the co-organizer, relying on their mature computing power platforms to provide all-round technical support.

In terms of topic guidance, the competition carefully designed three parallel tracks with both technical challenges and integration value around the annual theme. The first track is “Smart Port and Logistics Optimization”. The second track is “AIGC Empowering Chinese Cultural Heritage”. The third track is “AI-driven cross-border e-commerce and supply chain finance”. The design of these topics closely combines technological exploration with serving the common interests of both sides of the Taiwan Strait, effectively avoiding the limitations of purely technical competitions and ensuring that the event has a strong integration orientation from the very beginning.

In terms of the design of the collaborative co-creation mechanism, the competition has adopted a series of institutional measures to ensure in-depth interaction between young people on both sides of the Taiwan Strait. First, enforce mandatory mixed formations. Second, build a structured collaboration process. Finally, relying on the integrated AI collaboration platform of the co-organizer, it provides all teams with unified cloud computing power, shared data sets, collaborative programming environment and project management tools, completely eliminating technical barriers to cross-regional collaboration, allowing young people on both sides of the Taiwan Strait to seamlessly connect and work side by side in the same digital space, thereby establishing a strong team identity and personal friendship in the process of jointly solving problems.

In terms of building a value empowerment system, the competition is committed to creating a complete value chain from participation to personal development. At the level of career development, the competition has signed talent cooperation agreements with more than 50 well-known mainland technology companies, port groups and financial institutions, providing the winning team members with “through-train internships” and final qualifications for school recruitment; at the level of project incubation, the competition has established a million-level “Cross-Strait Youth AI Innovation Fund” to provide seed-round investments for winning projects with commercial potential, and recommend them to settle in national-level cross-strait youth entrepreneurship bases in Xiamen, Fuzhou and other places for free, and enjoy a full set of incubation services.

5. Path suggestions for cross-strait youth AI innovation competitions

Based on the aforementioned analysis of the development status and dilemma of cross-strait youth AI competition, as well as the construction of the “issue guidance-collaborative co-creation-value empowerment” model and specific cases, this study aims to promote the healthy and sustainable development of this new communication paradigm and proposes the following four interrelated path suggestions.

First of all, top-level design and policy guidance for the event should be carried out from the central level. The core is to establish an authoritative issue release and certification mechanism. Secondly, institutional design must be used to ensure the in-depth collaboration and co-creation of young people from both sides of the Taiwan Strait in the game. The key is to promote mandatory mixed formations and structured collaboration processes. Furthermore, there is an urgent need to build a full-chain value empowerment system from competitions to personal development. The core lies in institutionally linking the competition experience with the long-term development opportunities of Taiwanese youth in the mainland. Finally, we should promote the joint construction of a national-level “Cross-Strait Youth AI Innovation Collaboration Cloud Platform” to serve as the technical base and community carrier for all competitions.

Keywords: cross-strait relations, artificial intelligence, youth exchanges, event design, integrated development

Algorithmic power gap and potential dilemmas in rural revitalization: theoretical analysis and situational deduction

Fri, 13 Jun 2025 00:00:00 +0000

Author: Dylan Chiang

Summary

This article uses the methods of theoretical analysis and situational deduction to systematically explore how artificial intelligence (AI) causes two potential dilemmas: algorithmic bias and power gap in the context of rural revitalization. Research points out that this problem is rooted in the existing digital inequalities and social inequality structures in rural areas, and may further solidify the marginalized status of disadvantaged groups through resource allocation and public services. The conclusion is that without a prudent framework centered on local participation and data governance, AI technology may erode the goals of equity and inclusiveness in rural revitalization.

Keywords: artificial intelligence, algorithm bias, rural revitalization, digital labor, data governance

1. The risk of AI bias in rural resource allocation and public services

Artificial intelligence (AI) is regarded as a potential tool to improve the efficiency of rural governance and promote equitable distribution of resources. However, in the unique rural context of Taiwan, the introduction of AI without careful consideration of its inherent limitations and social implications may not only fail to eliminate existing gaps, but may instead create or exacerbate prejudice, posing a potential threat to rural revitalization.

The decision-making quality of an AI system highly relies on the data it learns. However, in rural areas of Taiwan, especially remote mountainous areas, outlying islands, or communities with a very high proportion of elderly people (such as Yunlin Taixi, Pingtung Manzhou, etc.), the popularity of digital infrastructure, residents’ digital access capabilities, and the activity of data production systems are still significantly “digitally weak” compared to urban areas. This directly leads to the fact that rural data that can be used for AI training may face the dilemma of “little quantity and poor quality”, or even a fundamental “absence of data”.

Just imagine, if an AI system is used to evaluate the qualifications for natural disaster relief in agriculture and fishery, its main training data comes from those farmers and fishermen who are highly informatized, good at using online systems to report disaster damage, and whose crops or breeding types are more mainstream marketing channels. Then, for those older farmers and fishermen who still rely on traditional farming methods, are not familiar with digital tools, or grow/raise products with local characteristics but small market sizes, their data may be difficult to be fully captured or correctly interpreted by the system. In this case, the AI model may make unfavorable judgments about these “data invisible people” from the beginning due to insufficient data representation and systematic bias, putting them in a more vulnerable position in resource allocation. This exactly confirms that algorithmic bias often stems from the existing “truth of inequality” in society. In rural areas, this “inequality” is first reflected in the availability of data and the ability to truly reflect the appearance of the land.

The development and verification processes of many AI models currently used in public services often take urban environments as the main scenarios, and their built-in logic and parameter settings may unconsciously have the color of “urban centralism” or “mainstream group defaults.” If this type of model is directly implemented in rural areas without sufficient local adaptation and contextualization considerations, it may be “blind” or “incompatible” with the unique social and economic structure of the countryside, multicultural features (such as the traditional norms of aboriginal tribes, the industrial characteristics of Hakka settlements, and the living needs of new resident communities) and the heterogeneous needs of residents.

For example, if an AI system used to optimize public transportation routes and schedules in urban areas is simply applied to rural areas with vast territory, scattered settlements, and special travel needs of the elderly population (such as bus services in the Chiayi Mountains), it may be unable to understand the special transportation needs of rural residents for flexible reservations, ride-sharing services, or specific periods of time (such as agricultural product harvesting periods, temple fairs), and make resource allocations that are not in line with actual benefits. This neglect of local context essentially constitutes a structural bias against the particularity of rural areas, making it difficult for AI’s “wisdom” to truly respond to the real pain points of rural areas.

When AI systems process information involving vulnerable groups in rural areas (such as the elderly living alone, single-parent families with financial difficulties, people with physical and mental disabilities, new resident women with limited digital capabilities, etc.), even if there are no clear discriminatory rules, the underlying data patterns and algorithm logic may have hidden negative labels or resource crowding effects that are not easily noticeable. Drawing on the analytical perspective of “poverty-phobia”, AI may inadvertently replicate society’s stereotypes or implicit biases against specific groups in seemingly objective data analysis.

For example, if an AI system is used to screen social welfare or emergency assistance application eligibility, if in its training data, the cases of “successful” receiving subsidies are more likely to present some “typical” dilemma narratives or easily quantifiable indicators of difficulty. Then, for those disadvantaged cases in rural areas who are in the same difficult situation, but whose plight is more complex and difficult to present through standardized data, or who do not meet the mainstream society’s imagination of “ideal help seekers”, the AI system may give them a lower priority or even exclude them from potential service targets because their characteristics deviate from the “normal” model. This kind of implicit discrimination based on algorithms will make those disadvantaged groups in the countryside who are already in “invisible corners” face the risk of further marginalization and deprivation of resources in the AI era, running counter to the goal of rural revitalization to promote inclusion and equity.

2. The operation and inequality of algorithmic power in rural context

The application of AI systems in rural areas not only brings potential risks of bias, but may also create new asymmetries in power relationships. The design, deployment and operation process of algorithms are often dominated by specific technical elites, policy makers or business institutions, and the rural community itself has relatively weak voice and control in it. This power gap may make AI a tool to strengthen existing inequalities and even create emerging relationships of dominance.

The complexity and technical threshold of AI systems constitute an insurmountable barrier for many rural residents. Whether it is the underlying logic of AI decision-making, the way data is collected and used, or the explanation and appeal channels for system output, rural communities are often in a disadvantaged position due to information inequality. For example, when a local government introduces an AI system to assist agricultural land use planning or disaster potential assessment, ordinary farmers may have difficulty understanding how the system makes judgments, let alone effectively questioning its decisions or participating in system improvements. This is consistent with what Veale et al. have observed in the public sector. Even civil servants may lack a full understanding of the operation of AI systems, resulting in over-reliance or selective adoption. In rural contexts, this information gap may be more serious.

In rural areas of Taiwan, many elderly people or residents with relatively low education levels have limited digital literacy. If the operation interface of the AI system is not friendly enough, or the relevant training and instructions are insufficient, they are more likely to be marginalized in the AI era. AI technology that seems to be designed to “empower” rural areas may become a new “empowerment” mechanism if it lacks sufficient consideration and support for the abilities of local users, reducing rural residents from technology users to passive data providers and decision-making recipients.

The design and deployment process of AI systems often lacks substantial participation of rural communities. Decision-making power is often concentrated in the hands of central government, local government technocrats, or external AI solution providers. The local knowledge, practical needs and value concerns of rural residents, local organizations, and even grassroots civil servants may be ignored or simplified in the technology-led development process. This echoes Sampath’s concerns about the insufficient participation of local communities in the development of AI in countries in the Global South and their vulnerability to dominance by external forces.

For example, in the process of promoting “smart agriculture”, if the development of AI systems focuses too much on pursuing the single goal of increasing output or reducing costs, while ignoring the multiple values that small farmers attach to ecological sustainability, agricultural product quality, or rural cultural inheritance, it may lead to a disconnect between technical solutions and local needs. Many rural areas in Taiwan have unique natural environments and cultural features (such as the traditional areas of aboriginal tribes and the cultural landscape of Hakka villages). If the planning of the AI system fails to fully incorporate the opinions of local residents and respect their lifestyle and value choices, it may lead to questions about “Whose countryside? Whose AI?” and even rights conflicts over “Who does AI serve?” This risk of being “determined” will seriously weaken the subjectivity of rural communities and the momentum for local independent development.

When AI systems in rural areas mainly rely on external sources (such as large urban technology companies or multinational enterprises) to provide technology, platforms and data analysis services, we need to be alert to the risks of “technological colonization” that it may bring. Externally led AI solutions may carry with them the values and business logic of their developers (usually urban elites or Western cultural backgrounds), which may not be consistent with the local context of rural areas. This analogy to the dilemma of “digital colonization” or “data dependence” that countries in the Global South may face as Sampath refers to it may also appear in different forms in urban-rural relations.

For example, if the health passbook AI system in rural areas is trained mainly based on urban population data, its disease diagnosis model or treatment recommendations may ignore the impact of rural-specific genetic background, living habits or environmental factors on the disease, thus affecting its applicability and accuracy in rural areas. Furthermore, over-reliance on standardized, externally provided AI systems may also lead to the marginalization or even elimination of valuable knowledge accumulated in rural areas (such as traditional farming methods, local herbal knowledge, operational experience of community mutual aid networks, etc.). Zajko emphasized that sociology should pay attention to how AI affects power relations. In the rural context, this also includes an examination of the ebb and flow of power between local knowledge systems and external technological systems. One of the goals of rural revitalization is to activate local characteristics and resources. If the introduction of AI fails to effectively integrate with local knowledge and instead crowds out local wisdom, it will run counter to the original intention of rural revitalization.

In short, if the application of AI in rural areas fails to face up to and actively deal with the unequal operation of algorithmic power caused by problems such as technical thresholds, insufficient participation, and external leadership, AI will not only be difficult to become a tool to promote equitable rural development, but may deepen the existing power structure, causing rural communities to face new dilemmas and challenges in the digital age.

3. Potential impact on rural revitalization goals

Taiwan’s rural revitalization policy, whether it is rural regeneration in the early days or the local revitalization emphasized in recent years, has as its core goals the promotion of balanced regional development, revitalization of the local economy, and improvement of quality of life, with a special emphasis on social equity and inclusiveness, striving to allow everyone in the countryside to share the fruits of development. However, the risks of bias hidden in the rural applications of AI analyzed above are inconsistent with the operation of power. If they are not effectively controlled, they may have a direct and far-reaching negative impact on these noble policy goals.

Social equity and inclusiveness are the cornerstones of rural revitalization policies, which aim to narrow the gap between urban and rural areas and ensure equal opportunities among different groups within the countryside. However, the existence of algorithmic bias may cause AI systems to unconsciously replicate or even strengthen existing social inequalities in resource allocation and public service decisions, thus eroding the basis of policy fairness.

For example, if a set of AI is used to evaluate the priority of subsidy for “local creation” proposals, Systems whose training data or built-in evaluation indicators are overly focused on quantifiable economic benefits, technological innovation, or past successful urban cases may make rural proposals that are more focused on cultural heritage, ecological protection, community building, or caring for the disadvantaged (for example, a A traditional craft revitalization project led by tribal elders, or a local ingredient promotion cooperative initiated by mothers of new residents), due to their “non-mainstream” or “difficult to quantify” characteristics, are at a disadvantage in the ranking, causing subsidy resources to flow to projects that are more in line with mainstream imagination. Not only does this fail to promote diversity and inclusiveness in rural development, but it may stifle grassroots innovation that truly has local characteristics and social value.

Furthermore, as mentioned above, the potential discrimination of AI systems against rural vulnerable groups (such as the elderly, indigenous people, and economically disadvantaged households) may cause them to encounter hidden obstacles in accessing social welfare, medical resources, employment opportunities, or financial services. For example, if an AI system used to match long-term care resources in rural areas underestimates the actual needs of elderly people living alone in certain areas with inaccessible transportation due to data bias, or fails to fully consider the special family structure and care culture of indigenous tribes due to blind spots in the model design, it may lead to a misallocation of long-term care resources, causing the elderly who are most in need of help to be “forgotten.” This difference in resource allocation based on algorithms directly violates the social fairness principles of “taking care of the disadvantaged” and “leaving no one behind” emphasized in the rural revitalization policy, and may form a “fairness paradox in the AI era” - that is, technology in the name of pursuing efficiency and objectivity has instead created new injustices.

(2) The algorithmic power gap further marginalizes disadvantaged rural communities

The algorithmic power gap, that is, the insufficient voice and control of rural communities (especially disadvantaged groups) in the design, deployment, operation and supervision of AI systems, may further marginalize them in the process of rural revitalization and widen existing social gaps.

When the introduction of AI systems is mainly led by external technical elites or higher-level governments, and rural residents lack effective participation channels and capabilities, their needs and voices are easily ignored. For example, in the process of promoting “smart tourism”, if the establishment of AI systems (such as attraction recommendation, tourist behavior analysis) is only from the perspective of improving tourism output value, and fails to fully consult local residents’ opinions on tourism development models, environmental carrying capacity, and cultural impact, it may lead to uneven distribution of tourism benefits, and even trigger conflicts between local residents and tourism development, deteriorating the good intentions of rural revitalization.

What is even more worrying is that technical thresholds and information asymmetry may put vulnerable groups in rural areas in a position of being “powerless to resist” when faced with AI decision-making. They may not understand how AI can make decisions that go against them and lack effective avenues for appeal or redress. For example, if an AI system demarcates the traditional territory of an indigenous tribe as an area unsuitable for the development of a specific industry, but its decision-making basis is contrary to the tribe’s traditional knowledge and wishes, and if tribal members lack sufficient technological literacy and legal resources to challenge this decision, their development rights and interests may be infringed. In this case, the AI system not only failed to empower the countryside, but instead became a new dominant force, making disadvantaged communities even more speechless in the rural development agenda, and making their unique culture and needs more difficult to see and respect. The solidification of this power structure will reduce rural revitalization to a game for a few elites, and will not truly benefit all rural residents, especially those disadvantaged groups that need help most.

In short, algorithmic bias and power gap are like two sides of the coin of rural AI applications, jointly posing severe challenges to the goals of fairness, inclusiveness and local subjectivity in rural revitalization. If we fail to face up to and actively respond to these potential impacts, the introduction of AI technology may drift further away from the original intention of rural revitalization.

4. Conclusion and Policy Implications

(1) Summary of main arguments

Through a review and theoretical analysis of relevant core literature, this study aims to explore the algorithmic bias and power gap dilemmas that artificial intelligence (AI) may cause in resource allocation and public service decision-making in the context of rural revitalization. Based on the previous arguments, this study puts forward the following main arguments: The potential and risks of applying AI to rural revitalization coexist. Algorithmic bias and power gap are potential dilemmas that must be seriously faced. Although AI technology is highly expected to bring opportunities for efficiency improvement and resource optimization for rural development, its actual application in rural areas will inevitably encounter the risk of bias posed by data poverty and distortion, contextual blind spots in model design, and implicit discrimination against disadvantaged rural groups. At the same time, factors such as technical thresholds, insufficient participation mechanisms, and external technological dominance may also form new asymmetries in the operation of algorithmic power within villages. If these biases and power gaps are not effectively identified and managed, AI will not only be unable to achieve its original intention of promoting rural revitalization, but may instead become a new driver of exacerbating existing inequality and eroding social fairness.

These dilemmas are not purely technical defects, but complex problems deeply rooted in social structure, power relations and governance practices. The root cause of algorithmic bias is not just the “dirtyness” of the data or the “imperfection” of the model, but more deeply reflects what Zajko calls the “real basis of inequality” in society. The long-standing urban-rural gap, low digital life, uneven distribution of resources, and the disadvantaged position of specific groups in the social structure in rural areas may all be copied, continued, or even amplified unconsciously by AI systems. Similarly, the operation of algorithmic power is closely related to the existing local political and economic structure, the transparency and accountability of the governance model, and the participation ability of civil society. As Sampath observes in the Global South, without a sound governance framework and full respect for local contexts, the introduction of technology may actually reinforce existing power imbalances. Therefore, it will be futile to try to “repair” prejudice or “balance” power from a technical perspective only, while ignoring the underlying social structural factors and governance mechanisms.

(2) Policy Enlightenment

In order to deal with the algorithmic bias and power gap that may be caused by AI in rural applications, and to ensure that technological development can truly serve the goals of equity and inclusion in rural revitalization, this study proposes the following policy implications based on the analysis of core literature and considering the special context of rural Taiwan:

Data governance and local participation: Constructing a people-centered rural data ecosystem Data is the cornerstone of AI, but the poverty and bias of rural data are the source of AI bias. Therefore, the first priority is to establish a rural data governance framework that is ethical and emphasizes local participation. This includes:

Improve data representativeness and quality: Resources should be invested to improve digital infrastructure in rural areas, and more inclusive data collection methods (such as combining oral interviews, community field surveys, etc.) should be designed for digitally vulnerable groups such as the elderly, indigenous people, and new residents to ensure that their experiences and needs can be included in the data set. For example, when building an agricultural AI system, in addition to standardized production and sales data, “atypical” data such as small farmers’ local farming knowledge and traditional crop varieties should also be included.
Emphasis on data sovereignty and local community participation: We should learn from international concepts such as “Indigenous Data Sovereignty” and explore the establishment of a mechanism for rural communities (such as tribes, rural and fishing village community development associations) to participate in data collection, management, interpretation and application. For example, promote the establishment of “community data cooperatives” or “local data trust” models, so that rural residents can have greater informed consent and control over data involving themselves or their communities, and prevent data from being unilaterally seized or improperly used by external agencies.
Develop ethical guidelines for rural data: Based on the particularity of rural AI applications, clear ethical guidelines for data collection and use should be formulated to clarify data ownership, privacy protection, and bias risk assessment specifications to ensure that data applications are premised on promoting rural well-being.

The pursuit of algorithm transparency, accountability and explainability: opening the “black box” of AI decision-making The opacity of algorithms is a breeding ground for power gaps and biases. When the public sector introduces AI systems in rural areas, it must strive to promote transparency, accountability and explainability:

Promote the transparency of public sector AI systems: The government should establish a login and disclosure mechanism for AI systems. For AI systems used in rural resource allocation or public services, they should proactively disclose their design purpose, main functions, training data sources (without infringing privacy), and known limitations and potential risks.
Establish bias impact assessment and appeal mechanism: Before the deployment of AI systems and during operation, bias impact assessments should be conducted regularly, with special attention to their potential differential impact on rural vulnerable groups. At the same time, a convenient and effective complaint and relief channel should be established so that rural residents can receive immediate response and processing when they believe they have been unfairly treated by AI decision-making.
Exploring local interpretability methods: Given the difficulty that rural residents (especially the elderly) have in understanding complex technical explanations, communication methods that go beyond purely technical explanations should be explored. For example, visual tools, local languages, case descriptions, or translation through local opinion leaders (such as village chiefs and tribal leaders) can be used to make AI’s decision-making logic easier to understand and trust among rural communities.

Empowerment and critical thinking cultivation: improving AI literacy in rural areas Facing the advent of the AI era, it is crucial to improve the understanding and critical thinking of rural residents and grassroots workers about AI. This is the basis for narrowing the gap in algorithm power:

Promote AI literacy education in rural areas: Combining existing resources such as community colleges, villagers’ gatherings, and farmers’ and fishermen’s association promotion systems, we can set up easy-to-understand AI science courses and workshops for rural residents of different ages and backgrounds, so that they can understand the basic principles, common applications, potential risks, and their own rights of AI.
Strengthen the AI awareness of grassroots civil servants: Provide AI-related professional training to grassroots civil servants serving in rural areas, so that they can not only operate AI systems, but also understand the limitations of the system, identify potential biases, and play the role of “translator” and “gatekeeper” between AI decision-making and actual local needs to avoid blind obedience or abuse of AI.
Cultivation of critical participation skills: Encourage rural residents not only to be passive AI users, but also to become participants with critical thinking, who can actively explore problems in the local application of AI systems and actively make suggestions for improvement.

Encourage the development of context-sensitive and demand-oriented AI: let AI truly serve rural areas

Avoiding “one-size-fits-all” technical solutions and encouraging the development of AI applications that can truly respond to the local needs of rural areas is the key to eliminating blind spots in the model context and leveraging the positive benefits of AI.
Support bottom-up AI innovation: Resources and platforms should be provided to encourage rural communities, local organizations, local community colleges or returning youth to proactively propose and participate in the development of AI solutions with local characteristics and context sensitivity in response to practical problems faced by rural areas (such as elderly care, characteristic agricultural development, cultural inheritance, environmental monitoring, etc.).
Emphasis on participatory and collaborative design of AI design: In the early stage of the design of rural AI systems, the participation of multiple stakeholders such as rural residents, grassroots workers, and local experts should be included, and through workshops, focus discussions, etc., their needs, experiences, and values can be fully integrated into the system design to avoid the disconnect between technology and needs.
Establish a pilot and evaluation mechanism for rural AI applications: For new rural AI applications, small-scale pilots should be conducted first, and an effectiveness evaluation and ethical review mechanism involving the participation of local communities should be established. Continuous revision and optimization should be based on actual feedback to ensure that AI applications meet the real needs and long-term well-being of rural areas.

The importance of sociology and cross-disciplinary cooperation: integrating multiple perspectives to jointly govern AI The complexity of AI governance, as well as its deep-rooted nature in social structures, make it difficult for a single discipline or department to deal with it alone. Zajko emphasizes the important role of sociology in understanding and responding to bias and inequality in AI, a perspective that is particularly instructive for rural AI governance:

Strengthen the role of social sciences in AI governance: In the formulation of rural AI policies, systematic design and development, impact assessment, and ethical review, experts and scholars in social science fields such as sociology, anthropology, geography, and law should be actively included so that they can provide in-depth analysis and suggestions from multiple perspectives such as social structure, power relations, cultural context, and ethical norms.
Promote cross-field dialogue and cooperation between government, academia, industry and civil society: A regular cross-field communication platform should be established to allow AI technology developers, policy makers, rural practitioners, local residents’ representatives and academic researchers to have full dialogue, consultation and cooperation on the vision, challenges and paths of rural AI development, and jointly shape an AI governance framework that is responsible, inclusive and conducive to sustainable rural development.

5. Research Prospects

First, there is a call to deepen on-the-ground empirical research: from theoretical discussion to field cultivation.

The existing literature mostly discusses AI bias and governance challenges based on general principles or specific foreign cases. However, rural society in Taiwan has its own unique history, culture, industrial structure, and social network characteristics (for example, the traditional domains and governance models of aboriginal tribes, the industrial culture of Hakka settlements, the population structure and livelihood methods of western coastal agricultural and fishing villages, and the significant digital activities between different regions, etc.). How these local contexts interact with the introduction of AI technology to shape the specific forms of algorithmic bias, the transformation of power relations, and the actual impact on rural revitalization remains to be answered by in-depth local empirical research.

Therefore, future research should actively invest in rural fields in Taiwan and use qualitative research methods (such as in-depth interviews, focus groups, participant observation) and quantitative research methods (such as questionnaires, data analysis) to combine multi-level discussions.

For example, we can conduct in-depth case studies on AI application cases that have been implemented in rural areas (such as smart agricultural guidance systems, remote long-term care resource matching platforms, local revitalization subsidy evaluation tools, etc.), and interview system developers, policy makers, grassroots executives, and affected rural residents to understand their understanding of the AI system, usage experience, perceived benefits and difficulties, as well as their agency and negotiation process. Secondly, the bias impact assessment and power interaction analysis focus on specific cases. In terms of specific empirical research directions, in the future, more detailed bias impact assessment and power interaction analysis can be conducted for specific rural AI application cases:

Bias impact assessment: You can learn from Curto et al.’s analysis method of “poverty phobia”, but further combine it with local data and situations to examine whether there are systemic biases in specific AI systems against rural elderly, indigenous people, new residents, low-income households, or digitally disadvantaged groups. For example, analyze whether the decision-making results of an AI model used for agricultural loan approval have a disproportionate negative impact among farmers with different socio-economic backgrounds or farming scales.
Power interaction analysis: You can use Zajko’s sociological perspective and Veale et al.’s insights into public sector AI practices to conduct an in-depth analysis of how power relationships between different actors (such as the central government, local governments, technology providers, local elites, ordinary residents, representatives of disadvantaged groups, etc.) are performed, negotiated, and reconstructed during the design, deployment, and operation of rural AI systems. For example, it explores how the voices of local communities are heard or silenced in the cracks between the technocratic system and commercial interests in smart rural policies promoted “from above.”

Through the above-mentioned empirical research, we can not only gain a deeper understanding of the real impact of AI in the rural context of Taiwan, but also provide a solid academic foundation and practical reference for formulating more targeted and effective algorithmic governance policies and developing AI applications that better meet local needs, thus ensuring that AI technology can truly become a positive force in promoting inclusive, equitable and sustainable development in rural Taiwan.

References

Curto, G., Jojoa Acosta, M. F., Comim, F., & Garcia-Zapirain, B. (2024). Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings. AI & Society, 39(2), 617-632.
Gehl Sampath, P. (2021). Governing artificial intelligence in an age of inequality. Global Policy, 12, 21-31.
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), e12962.
Kukutai, T., & Taylor, J. (2016). Indigenous data sovereignty: Toward an agenda. ANU Press.
Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-14).

[Author information] Dylan Chiang, National Institute of Development and Mainland China, Chinese Culture University, Taiwan, email:

Research on the usage behavior of generative artificial intelligence: taking a legislative assistant as an example

Sun, 01 Dec 2024 00:00:00 +0000

Summary

This study explores the use of generative artificial intelligence in the work of legislative assistants, and analyzes its impact on legislative work efficiency, decision-making quality, and work processes.

Reference errata (Errata, 2026-06-12)

After this paper was deposited, the author used the self-developed citation verification tool [citation-verify] ( ) to review all 47 references. The following clerical errors at the bibliographic level were found and were publicly corrected in accordance with academic practice. **The above errors do not affect the argument and conclusion of this paper. **

**DOI correction (original DOI is parsable, but points to other article): **

Entry	Original DOI (wrong)	Corrected DOI
Brooks, Longstreet, & Califf (2017)	`10.17705/1thci.00009`	`10.17705/1thci.00091`
De Vries, Tummers, & Bekkers (2018)	`10.1093/ppmgov/gvx003`	`10.1093/ppmgov/gvy001`
Wu & Lin (2016)	`10.1016/j.techfore.2016.06.011`	`10.1016/j.techfore.2016.06.028`

Signature correction:

Thomas, E., & Rogers, B. E. M. (1998) → should be Backer, T. E., & Rogers, E. M. (1998)
“Perceived usefulness… (n.d.). MIS Quarterly” → should be Davis, F. D. (1989), MIS Quarterly, 13(3), 319–340

In addition, the author/source information of 7 online source entries has been completed, and 2 duplicate entries have been deleted. The complete errata and verification methods can be found in [Actual Test Case 001] ( .

English Title

A Study of Generative Artificial Intelligence Use Behavior: Evidence from Legislative Assistants

Chapter 1 Introduction

1.1 Research background and motivation

(To be added)

1.2 Research purposes and questions

(To be added)

1.3 Research scope and limitations

(To be added)

Chapter 2 Literature Discussion

(To be added)

2.2 Technology Application in Political Work

(To be added)

2.3 Using behavioral theory

(To be added)

Chapter 3 Research Methods

3.1 Research design

(To be added)

3.2 Research objects and samples

(To be added)

3.3 Data collection method

(To be added)

3.4 Data analysis methods

(To be added)

Chapter 4 Research Results

4.1 Descriptive statistical analysis

(To be added)

4.2 Using behavior analysis

(To be added)

4.3 Analysis of influencing factors

(To be added)

Chapter 5 Discussion and Suggestions

5.1 Discussion of Research Findings

(To be added)

5.2 Practical suggestions

(To be added)

5.3 Research limitations and future research directions

(To be added)

Chapter 6 Conclusion

(To be added)

References

(To be added)

Appendix

(To be added)

From technological innovation to cultural inheritance: the role of AIGC technology in rural revitalization and cultural tourism products

Wed, 31 Jan 2024 00:00:00 +0000

Chapter 1 Introduction

1. Research background

In today’s context of globalization, cultural exchanges and cooperation across the Taiwan Straits have increasingly become an important way to promote common development. Mainland China and Taiwan have the same culture and race, and share a rich and profound historical and cultural heritage. From traditional art forms to folk cultural customs, these cultural assets are not only the emotional connection between people on both sides of the Taiwan Strait, but also valuable resources for rural revitalization and cultural industry development.

However, with the rapid development of modern society, rural areas are facing challenges such as population aging, economic backwardness, and cultural inheritance gaps. These issues not only affect the sustainable development of rural areas, but also put forward new requirements for cross-strait cultural exchanges and integration. Against this background, exploring effective rural revitalization strategies, especially how to use modern technological innovations to protect and develop cultural products, has become an important topic.

In recent years, the rise of artificial intelligence generated content (AIGC) technology has provided new possibilities for the innovation of cultural products. AIGC technology, especially tools such as ChatGPT and Stable Diffusion, has demonstrated its strong potential in fields such as text creation, image generation, and multimedia content production. These technologies can not only efficiently generate content that meets specific styles and needs, but can also promote the combination of traditional culture and modern creativity, providing new ideas for cultural tourism product innovation in rural areas. For both sides of the Taiwan Strait, how to effectively use AIGC technology to promote the innovation of cultural tourism products and rural revitalization can not only promote economic development, but also help deepen the cultural identity and emotional connection of people on both sides of the Taiwan Strait.

Generative AI, especially conversational AI models such as ChatGPT, has shown great potential in fields such as text creation, image art, and multimedia content production. These technologies can not only automatically generate text content that matches a specific style and theme, but can also create visually appealing images and even produce high-quality video and voice productions. For the common cultural inheritance and innovation of both sides of the Taiwan Strait, generative AI technology provides a new way to spread and develop traditional culture in an innovative form in modern society.

Both sides of the Taiwan Strait have rich cultural resources and unique cultural expressions, which are rich soil for the application of generative AI technology. Through these advanced technologies, cultural creators on both sides of the Taiwan Strait can break through the limitations of traditional media and transform cultural heritage into innovative products with global appeal. More importantly, generative AI technology can help cultural and creative industries in rural areas improve efficiency and expand their influence, thereby injecting new vitality into the local economy and promoting rural revitalization.

However, the application of AIGC technology in rural revitalization and cultural tourism products is still in its preliminary stages, and its potential and challenges have not yet been fully understood and explored. In view of this, this study aims to deeply analyze the role of AIGC technology in rural revitalization and cultural tourism product innovation, explore how to effectively use these technologies to promote the inheritance and development of local culture, and bring sustained economic and social benefits to rural areas.

Through in-depth research on AIGC technology, this study not only hopes to enhance the understanding of the application of this technology in cultural product innovation, but also aims to provide practical strategies and suggestions for policymakers, local managers, and creative industry practitioners to jointly promote the revitalization and prosperity of rural areas on both sides of the Taiwan Strait.

2. Research purpose

In contemporary society, the relationship between technological innovation and cultural inheritance has attracted increasing attention, especially in the fields of rural revitalization and cultural tourism product development. The rise of artificial intelligence generated content (AIGC) technology has brought new opportunities and challenges to this field. Therefore, this research will focus on the following core questions:

Understand the potential of AIGC technology: In-depth understanding of AIGC technology and its application potential in cultural inheritance and rural revitalization.

Analyze the impact of AIGC technology on cultural tourism product innovation: Through case studies, analyze the specific application and effect of AIGC technology in promoting local cultural tourism product innovation.

Propose AIGC technology integration strategy: Based on research findings, propose strategies and suggestions on how to effectively use AIGC technology to support rural revitalization and cultural tourism product development.

3. Research scope and methodology

With the profound impact of technological innovation on various fields of contemporary society, the application of AIGC technology in rural revitalization and cultural tourism product innovation has become a new field worth exploring. This study aims to gain an in-depth understanding of how AIGC technology promotes cultural inheritance and innovation, especially in improving the attractiveness and competitiveness of cultural tourism products in rural areas. To this end, this study will use the following categories and methodologies to conduct a systematic discussion.

Research scope
- This research focuses on the application of AIGC technology in rural revitalization and cultural tourism product innovation, including but not limited to the following key areas:
- AIGC technology analysis: In-depth understanding of AIGC technology principles including ChatGPT and Stable Diffusion and its application capabilities in generating text, images, videos, etc.
- Cultural tourism product innovation: Explore how AIGC technology can be applied to create or enhance cultural tourism products, including but not limited to travel guides, cultural displays, interactive experiences, etc.
- Rural revitalization strategy: Analyze the role and potential of AIGC technology in promoting rural economic development, cultural inheritance and community participation.
Methodology

To ensure the depth and breadth of the research, this research will adopt the following methods:

Literature review: Systematically review relevant academic literature, technical reports and case studies to obtain the basic knowledge and current status of AIGC technology and its application in related fields.
- Case studies: Select representative AIGC technology application cases, especially successful practices in rural revitalization and cultural tourism product innovation, for in-depth analysis.

Through the application of the above methodology, this study will comprehensively explore the role of AIGC technology in current and future rural revitalization and cultural tourism product innovation, and propose practical strategies and suggestions to promote the harmonious development of technology and culture.

Chapter 2 AIGC Technology Analysis and Transnational Development Trends

1. Overview of AIGC technology

In the field of artificial intelligence, AIGC (artificial intelligence generated content) technology is quickly becoming a revolutionary branch that uses advanced machine learning algorithms to automatically create text, image, audio and video content that is comparable in quality and diversity to human creators.

Definition and classification

AIGC technology can be divided into the following categories based on the type of content it generates:
- Text generation: Use natural language processing (NLP) algorithms, such as GPT-3, to create new text content.
- Image generation: generate static images from text descriptions through algorithms, such as Stable Diffusion.
- Audio generation: algorithms for creating musical compositions or simulating human voices, such as WaveNet.
- Video generation: Algorithms that combine image and text generation techniques to create animations or simulate real-world scenes.

Technical principles

• 1. Transformer architecture

• One of the core principles of AIGC technology is the Transformer architecture, which has played a key role in the advancement of natural language processing (NLP) and other AI fields in recent years. The converter architecture was first proposed by Vaswani et al. in 2017. Its innovation lies in the use of a self-attention mechanism to process sequence data, which enables the model to learn long-distance dependencies in the sequence more effectively.

• At the core of the transformer architecture is a self-attention mechanism, which allows the model to take into account all other elements in the sequence when processing each element of the sequence, thereby obtaining a more comprehensive contextual understanding. This is crucial to generating coherent and contextually relevant content.

• Models based on the transformer architecture, such as the GPT (Generative Pre-trained Transformer) series, learn rich language patterns and knowledge through large-scale data pre-training, thereby being able to generate high-quality text content under unseen cues. This method of pre-training and fine-tuning further improves the model’s performance on specific tasks, allowing it to demonstrate powerful capabilities in multiple application fields such as text generation, language translation, and content summarization.

• The converter architecture not only promotes the development of the field of natural language processing, but also provides a solid foundation for the advancement of AIGC technology, allowing it to play an important role in cultural inheritance, education, entertainment and other fields.

• 2. Diffusion Model

• Recently, in the field of image generation, diffusion models (such as Stable Diffusion) have shown their powerful generation capabilities. These models generate new images by gradually introducing random noise and then learning to reverse the process to reconstruct the data. This stepwise diffusion and reduction process enables the model to create high-quality and detailed images, which has broad applications in artistic creation and visual content generation.

• Both architectures have promoted the development of the AIGC field, and their innovations in model design and learning strategies have provided strong technical support for generating various types of new content. With further research and optimization of these models, we can foresee that AIGC technology will play a more important role in the cultural and creative industries.

2. Development trends of AIGC technology in China and the United States

With the rapid development of AIGC (artificial intelligence generated content) technology, its potential in personalized healthcare, educational development, creative teaching methods, and industry applications has gradually been recognized globally, especially in China and the United States. These technologies not only promote the development of innovative educational methods and personalized learning experiences, but also promote high-quality development of specific industries (Chen et al., 2024a; Chen et al., 2024b; Chen et al., 2024c; Sun, 2024; Xu et al., 2024).

In the field of personalized healthcare, Chen et al. (2024a) explored how to use mobile AIGC technology to realize human digital twins, thereby leading the revolution in personalized healthcare. This technology collects and analyzes individual health data to provide users with customized health management solutions, demonstrating AIGC’s great potential in improving the efficiency and quality of medical services. The field of education has also witnessed the significant impact of AIGC technology. Chen et al. (2024b) demonstrated through a systematic literature review how AIGC can empower educational development, especially in promoting learner creativity and engagement. In addition, research by Chen et al. (2024c) shows that multimedia teaching using AIGC-driven animated concept maps can effectively improve students’ creativity.

In terms of industry applications, Sun (2024) discussed how AIGC technology promotes the high-quality development of Guangzhou’s customized home industry. By leveraging AI-generated content to optimize the design and production process, we can provide customers with more personalized and innovative home solutions. Similarly, Xu et al. (2024) elaborated on the application of edge-cloud generated AI in mobile networks, especially in providing AIGC services, bringing innovative service models and business opportunities to mobile networks.

These studies show that AIGC technology is continuously promoting innovation and high-quality development in education, healthcare and specific industries, whether in China or the United States. With the continuous advancement of technology and the expansion of application fields, AIGC technology is expected to play a more important role globally.

Chapter 3 Application of AIGC technology in local cultural tourism products

1. Application cases of large language models (LLMs)

(1) Automatic generation of travel guide

In today’s tourism industry, large language models (LLMs) such as GPT-3 are revolutionizing the way travel guides are created, providing tourists with more personalized, in-depth, and instantly updated travel information. By learning a large amount of travel-related data and user interactions, these models can automatically generate travel guides containing rich content, thereby improving tourists’ travel experience.

1. The meaning of automatically generated travel guide

The automatically generated travel guide not only provides standard tourist attraction information, but also digs into the cultural background, historical stories and local lifestyle of the destination. This kind of guide can provide customized suggestions based on tourists’ specific interests and needs, such as recommending hidden attractions, local specialties, festivals, etc., making the travel experience richer and more personalized.

2. Advantages of real-time updates

Traditional travel guides often fail to reflect the latest conditions of the destination in real time. However, LLMs can instantly process and integrate the latest travel trends, weather changes, traffic information, etc. to ensure the timeliness and accuracy of travel guides. This is a great convenience for tourists who need to cope with changes in peak travel periods and emergencies.

3. Personalization and interactivity

The large language model can also further customize the content of travel guides by analyzing tourists’ search history, preferences and feedback. For example, for tourists who love hiking, the system can provide detailed hiking routes, equipment recommendations and safety tips; for food lovers, it can select local must-try delicacies and hidden gourmet shops. In addition, these models can interact with tourists in a question-and-answer format and respond instantly to specific queries from tourists, further enhancing the personalization and interactivity of travel guides.

(2) Real-time translation and language conversion: breaking language barriers

1. Innovation in real-time translation

The real-time translation function of LLMs provides unprecedented convenience for cross-language communication. This not only covers the instant translation of daily conversations, but also includes the instant understanding of local characteristics, historical and cultural explanations. Tourists can receive accurate translations in real time through smart devices, thereby gaining a deeper understanding of local history, culture and customs. The application of this technology not only enhances the depth and breadth of tourism, but also allows tourists to explore regions with different languages and cultures more confidently.

2. Diversity of language conversion

In the context of globalization, information on tourism products and services needs to be translated into multiple languages to meet the needs of travelers from all over the world. The language conversion function of LLMs allows tourism-related enterprises and organizations to quickly convert their services, product introductions, user guides, etc. into multiple languages and ensures the naturalness and cultural accuracy of the translation. This not only improves the accessibility of services, but also greatly improves the user experience, allowing every traveler to obtain rich travel information in their native language.

3. Enhance cultural exchanges

Through real-time translation and language conversion technology, tourists are no longer restricted by language barriers and can communicate more freely with local residents and share each other’s culture and lifestyle. This kind of exchange not only enriches the travel experience, but also promotes understanding and respect between different cultures. Furthermore, this also brings opportunities for deeper cultural integration and international development to tourist destinations.

(3) Cultural Heritage Narrative: Revitalizing History and Connecting the Future

1. Innovation in digital narrative

LLMs can transform rich historical materials and cultural heritage stories into engaging narratives that not only include textual descriptions, but also incorporate audio, visual elements, and even interactive experiences. This multi-dimensional presentation method allows ancient legends and historical events to be reproduced in front of contemporary audiences, improving the attraction and educational value of cultural inheritance.

2. Enhancement of education and display functions

In museums and cultural exhibitions, LLMs can serve as tour guides, providing multilingual narration and customized narrative routes based on the backgrounds and interests of different audiences. Such personalized tours enable each visitor to gain a deeper cultural experience, while also providing educators with a new teaching tool to impart historical and cultural knowledge in a more vivid way.

3. Provision of interactive experience

The application of LLMs can also be extended to create interactive experiences, for example, through virtual reality (VR) or augmented reality (AR) technology, allowing visitors to deeply experience historical scenes or traditional lifestyles. This immersive narrative form not only makes history accessible, but also provides new ways to protect and promote intangible cultural heritage.

4. Preservation of cultural diversity

Through the narrative capabilities of LLMs, stories of niche cultures and dialects that may have been gradually forgotten can be preserved. These models can process and generate text in multiple languages, even those with a small number of speakers, helping to preserve linguistic and cultural diversity and encourage more people to understand and appreciate these unique cultural features.

2. Comprehensive application cases of Vincentian diagrams and Tusheng diagrams

(1) Cultural and creative product design: the combination of creative photography and drawing technology

Case Study: Traditional and Personalized Product Innovation

1. Combination of creative photography and dragon image

In this case, the designer first collected photos from users of personal or emotionally connected scenes. Subsequently, Tu Sheng Tu technology was used to incorporate the legendary elements of the Chinese dragon into these photos, creating a series of new images that are both mysterious and full of cultural connotations. These images not only show the power and dignity of the dragon, but also bear the imprint of the user’s personality.

2. Diversified design of cultural and creative products

These creative images were further used in the design of key rings and cultural fans. As a small item for daily use, the key ring has become a carrier of personal stories through its unique design. The cultural fan combines traditional craftsmanship with modern design. It is not only a practical tool for keeping cool in summer, but also a work of art that can be displayed.

3. The connection between culture and market

This kind of design, which combines traditional cultural symbols and personalized elements, not only enriches the types of cultural and creative products, but also enhances the cultural value and market appeal of the products. As part of cultural tourism products, these exquisitely designed key rings and fans can be used as travel souvenirs or cultural gifts, conveying global consumers’ understanding and respect for Chinese culture.

Through this case, we can see the potential of AIGC technology in the design of cultural and creative products. It can not only present traditional culture to modern consumers in a new form, but also provide unlimited creative space for personalized products. In the future, with the continuous development of technology, we look forward to seeing the birth of more creative cultural and creative products that combine tradition and modernity.

(2) Cultural and creative product design: the combination of Wenshengtu technology and aboriginal culture

Case Study: Innovative Design of Aboriginal Girl Figures

1. Application of Vincentian diagram technology in product development

In this case, the designer used Vincentian drawing technology to transform the image of aboriginal girls and their cultural characteristics into a series of figure design concept drawings. These concept drawings depict girls wearing traditional clothing and distinctive accessories. Each figure shows the unique culture and artistic style of different tribes.

Based on these concept drawings, actual girl figures were carefully crafted and randomly assigned into blind boxes. This kind of blind box not only attracts many enthusiasts as a collectible, but also becomes a new way to promote aboriginal culture. When consumers purchase blind boxes, they experience the excitement of exploring the unknown and discovering the beauty of culture.

3. Interaction between culture and market

The launch of these figurine blind boxes not only provides an innovative platform for the inheritance and promotion of Aboriginal culture, but also creates interesting interactions between Aboriginal art and modern consumer culture. Their sales in cultural exhibitions, tourist souvenir shops or specialized cultural and creative product stores not only increase the diversity of cultural and creative products, but also provide consumers with more choices.

In this way, Wenshengtu technology not only opens up new possibilities for the modern expression of aboriginal culture, but also brings new vitality to the cultural and creative commodity market. This innovative product that combines traditional culture and modern technology demonstrates the strong application potential of AIGC technology in the cultural and creative industries, and indicates that there will be more innovative directions and development space for cultural and creative product design in the future.

(3) Travel souvenir design: the integration of creative photography and traditional art

Case analysis: Creative combination of Dunhuang Feitian and personal image

1. Application of picture-taking technology in personalized creative photography

In this case, the designer used Tushengtu technology to creatively combine personal photos provided by consumers with the classic pattern of Dunhuang Feitian. Widely recognized for its elegant posture and rich colors, the Dunhuang Flying Apsara is a classic symbol of ancient Chinese Buddhist art.

2. Create personalized works of art

Through the application of AIGC technology, the user’s image is cleverly integrated into the picture of Dunhuang Flying Sky, as if the user himself has become the fairy floating among the murals. This kind of creative photography not only shows the user’s personality, but also presents traditional art to modern people from a new perspective.

3. Apply creative photography to cultural and creative products

Creative photography works can be further applied to various travel commemorative products, such as customized posters, clothing prints, mobile phone cases, and key rings. This not only allows consumers to own a unique piece of art, but also allows traditional art to gain new life in contemporary life.

Personalized creation implemented through Tushengtu technology not only brings fresh experiences to consumers, but also provides merchants with more marketing opportunities and market expansion possibilities. This innovative business model not only increases consumer satisfaction, but also promotes the development of local cultural industries and contributes to improving the brand image and sales performance of tourist destinations. With the advancement of technology and the introduction of innovative thinking, future travel souvenirs will be more diversified and better able to meet the market trends of personalization and customization.

Chapter 4 Conclusion and Outlook: Integrating AIGC into future rural revitalization strategies

1. The profound impact of AIGC technology on rural revitalization

1. Dual promotion of cultural inheritance and innovation

AIGC technology provides a new way for cultural inheritance in rural areas. Through large language models and image generation technology, traditional culture can be digitally preserved and re-presented to modern audiences in innovative forms. This not only helps protect endangered cultural heritage, but also injects new vitality into the development of cultural and creative industries.

2. Transformation and upgrading of economic development model

The traditional rural economy mainly relies on agriculture and simple handicrafts, and the introduction of AIGC technology has brought new economic growth points to rural areas. Through intelligent cultural tourism product development, personalized tourism services and innovative cultural and creative product design, rural areas can attract more tourists and investment and achieve diversified economic development.

3. Talent cultivation and skill improvement

The popularization of AIGC technology also provides new opportunities for talent training in rural areas. Local residents can improve their skills and participate in the development of the cultural and creative industry by learning and mastering these new technologies. This not only helps solve employment problems in rural areas, but also lays a talent foundation for local sustainable development.

2. Comprehensive evaluation of the impact of AIGC technology on cultural products

Technology improves market competitiveness

1. Product differentiation and personalization

AIGC technology enables cultural tourism products to achieve a high degree of personalization and differentiation. Through the creative capabilities of AI, each product can be customized according to consumers’ needs and preferences, which greatly enhances the market competitiveness of products and consumer satisfaction.

2. Cost-effectiveness optimization

Compared with traditional design and production methods, AIGC technology can significantly reduce product development time and costs. Designers can quickly generate multiple design solutions and adjust them based on market feedback, which makes product development more flexible and efficient.

3. Improvement of market response speed

The rapid iteration capability of AIGC technology enables companies to respond to market changes and consumer needs faster. When market trends change, companies can quickly adjust product design and marketing strategies to maintain a competitive advantage.

4. Innovative content generation capabilities

In today’s context of rapid digitization and globalization, AIGC technology can quickly generate creative content corresponding to specific cultural themes or styles based on user needs and market trends, including images, videos, texts and other forms. This flexible and efficient content generation capability enables cultural products to quickly adapt to market changes and satisfy consumers’ pursuit of freshness and personalization.

5. Combination of cultural inheritance and modern innovation

By applying AIGC technology, cultural enterprises and creative workers can break through the limitations of traditional creative output, explore new artistic expression and design concepts, and create unique cultural products. This not only helps maintain cultural diversity and innovation, but also provides more possibilities for the combination of cultural heritage and modern art.

Taken together, AIGC technology is having a profound impact on the design, production and sales of cultural products, and its role in promoting product innovation and enhancing market competitiveness cannot be ignored. In the future, with the continuous advancement of AI technology and the expansion of its application scope, AIGC technology is expected to become a key driving force in promoting the development of the cultural industry.

3. Opportunities and challenges of AIGC technology in rural revitalization strategy

1. Opportunity analysis

(1) Development opportunities brought by technology popularization

With the popularization of 5G networks and cloud computing technology, digital infrastructure in rural areas has been significantly improved. This provides technical support for the application of AIGC technology in rural areas, allowing local areas to better utilize these advanced technologies to develop cultural and creative industries.

(2) The driving role of policy support

The government’s policy support for rural revitalization and cultural industry development provides a good policy environment for the application of AIGC technology. Relevant financial support and preferential tax policies help reduce the threshold and cost of technology application.

(3) Growth of market demand

As consumers’ demand for personalized and cultural experiences grows, the market potential for cultural tourism products in rural areas is huge. AIGC technology can help locals develop more attractive and competitive products to meet market demand.

2. Challenge Analysis

(1) Technical threshold and talent shortage

Although AIGC technology has huge application potential, its technical threshold is relatively high and requires professional technical talents to operate and maintain. Rural areas often face talent shortages, which can limit the effective application of technology.

(2) Capital investment and cost control

The application of AIGC technology requires certain capital investment, including equipment purchase, software licensing, personnel training, etc. For rural areas with relatively poor economic conditions, how to balance input and output and control costs is an important challenge.

(3) Balance between cultural protection and innovation

While using AIGC technology for cultural innovation, how to protect and inherit the authenticity of traditional culture is an issue that requires careful consideration. Excessive technologicalization may lead to the distortion or commercialization of culture, which requires finding an appropriate balance between innovation and protection.

4. Prospects for future research and technology development

(1) Foreseeing the future of technology and applications

With the advancement of artificial intelligence technology, especially multi-modal AI large models, there will be more in-depth development of technology and applications in the field of AIGC in the future. Multimodal AI models better understand and generate human language and behavior by integrating and processing multiple types of data—such as text, images, sounds, and videos. We can expect that the following research directions will form important trends in the future:

1. Enhancement of contextual understanding

Future multimodal AI models will be more powerful in understanding the context of user inquiries, which will allow them to generate more nuanced and contextual responses, thereby providing a more complete interactive experience.

Future AIGC technology will be able to generate content across multiple modalities. For example, it can generate an image from a description and match the image with appropriate background music and text descriptions, creating a new, integrated multi-modal narrative.

3. Enhanced interactivity

Multimodal AI will be able to adjust content based on user interaction, such as generating real-time feedback based on user actions in a virtual reality environment, or providing customized guidance for user performance in interactive learning.

(2) Exploration of ethics, law and policy

As AIGC technology increasingly penetrates into all levels of personal life and social operations, becoming an indispensable part of the creative industry and daily communication, related ethical, legal and policy issues have gradually surfaced. Future research in this area needs to pay special attention to the following aspects:

1. Data privacy protection

With the development of AIGC technology, the collection and use of personal data has become more and more common. This makes data privacy protection an urgent issue that needs to be solved. Future research needs to conduct an in-depth analysis of how AIGC technology affects personal privacy rights and find solutions to balance technological innovation and personal privacy protection.

2. Intellectual property disputes

AIGC technology can generate a large amount of original content, but the ownership of the original rights of these contents has not yet been clearly defined by law. Future academic discussions need to conduct in-depth research on how to reasonably allocate intellectual property rights to works created by artificial intelligence, so as to provide reference for the formulation of relevant legal policies.

3. AI ethical issues

In addition to technical issues, the development of AIGC technology also brings social issues such as AI ethics. It is necessary to actively explore the ethical responsibilities of AI in the creation process through interdisciplinary cooperation, as well as the possible social impact of AI output content.

4. Needs for relevant policy formulation

While AIGC technology is developing rapidly, relevant policy formulation is necessary to guide and regulate the healthy development of technology. Future research should focus on policies that are being developed or need to be improved and make constructive policy recommendations based on practical experience and academic analysis.

5. Transnational legal coordination

Due to the global nature of AIGC technology, there may be legal differences in data governance and intellectual property rights in different countries. Research should focus on how the international community can jointly respond to this challenge through legal coordination and policy docking.

Through in-depth research on these important areas, we can not only foresee the development trends of technology, but more importantly, we can point out the strategic direction to deal with future challenges. This will provide a solid research foundation and strategic support for the role of AIGC technology in rural revitalization, making it an important force in promoting cultural, social and economic progress.

References

Chen, J., Yi, C., Du, H., Niyato, D., Kang, J., & Cai, J. (2024). A revolution of personalized healthcare: Enabling human digital twin with mobile AIGC. IEEE Network.

Chen, X., Hu, Z., & Wang, C. (2024). Empowering education development through AIGC: A systematic literature review. Education and Information Technologies.

Chen, X., Yahaya, W. A. J. W., Huang, Z., & Li, M. (2024). Enhancing Creativity with AIGC-Driven Animated Concept-Map Multimedia Instruction. Journal Title.

Sun, Y. (2024). Using Artificial Intelligence Generated Content Technology to Promote High-Quality Development of Guangzhou’s Customized Home Furnishing Industry. International Journal of Computer Science and Information Technology.

Xu, M., Du, H., Niyato, D., Kang, J., & Xiong, Z. (2024). Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services. IEEE Communications Surveys & Tutorials.

Collaborative innovation of cross-strait AI hardware in the face of technological blockade: Research on the cooperation model of Kinmen AI Computing Power Center

Sat, 20 Jan 2024 00:00:00 +0000

1. Introduction

1.1 The international background of the US blockade on AI chip technology and its impact on both sides of the Taiwan Strait

In today’s global economic system driven by technological innovation, artificial intelligence (AI) and semiconductor technology are undoubtedly at the forefront of the times. However, in recent years, the U.S. government has implemented a series of technological blockades and export control measures against China in these advanced fields based on geopolitical considerations and technological competition. This situation not only poses a direct challenge to China’s scientific and technological development, but also triggers attention and thinking about potential changes in the global scientific and technological innovation pattern. Especially in the field of AI chip technology, the United States has imposed strict export restrictions on leading companies including Nvidia and AMD starting in 2022 on the grounds of national security, preventing these companies from selling their most advanced AI chips to China. These chips are critical to the development of artificial intelligence because they provide the foundation for high-performance computing and are integral to applications such as big data analytics and smart manufacturing. The blockade measures demonstrate the United States’ firm stance in maintaining its technological hegemony and its determination to curb the rapid rise of China’s technology. The impact of this blockade on China is profound. It not only suppresses the technological innovation capabilities of Chinese enterprises, but also affects important development strategies promoted by the Chinese government, such as the “Manufacturing 2025” plan. In order to cope with these restrictions, China has to find alternative sources of technology or accelerate independent research and development of relevant domestic technologies.

1.2 Kinmen’s unique role in this pattern

For Taiwan, the U.S. blockade policy means both opportunities and challenges. As an important player in the global semiconductor manufacturing industry, Taiwan has access to these advanced technologies and has the ability to produce high-end AI chips. This advantage gives Taiwan the potential to become the mainland’s partner in breaking through the technological blockade. In particular, due to Kinmen’s special geographical location close to the mainland, the author proposed the idea of establishing an AI computing power center in Kinmen, and proposed the Kinmen AI computing power center as a possible cooperation platform to solve the challenges brought by the technological blockade. This not only has far-reaching significance for the development of cross-strait relations, but also provides a new perspective on global technological innovation trends.

In the current global technological competition, Taiwan’s role in facing the complex international technological landscape is particularly unique. Taiwan has always been an important base for the global semiconductor industry. It has a place in the world with its advanced process technology and chip design capabilities. In recent years, with the United States’ blockade of AI chip technology in mainland China, Taiwan’s strategic importance has been further pushed up.

Since Taiwanese companies can legally obtain and produce these high-end AI chips that are restricted in the mainland, Taiwan has become a potential partner of the mainland in this regard. This situation has opened up new possibilities for cross-strait cooperation in the high-tech field. Through such synergy, it will not only alleviate the mainland’s development bottlenecks in specific technology categories, but also help maintain Taiwan’s status and economic interests in the global semiconductor industry.

Kinmen has become an ideal place for such cooperation due to its geographical and historical particularities. As a frontier island on both sides of the Taiwan Strait, Kinmen is located in the Taiwan Strait, across the sea from the mainland. It has a strong cultural background in southern Fujian and is politically under the jurisdiction of the Taiwan region. Its unique geographical location allows it to play the role of a cross-strait bridge in politics, economy and culture. It is not only a link for exchanges between people on both sides of the strait, but also a potential window for scientific and technological cooperation.

Against the background of the US blockade of AI chip technology, the proposal to establish an AI computing power center in Kinmen can be regarded as a strategic deployment. Such a center will take advantage of Taiwan’s technological advantages and Kinmen’s geographical advantages, aiming to form a new model of scientific and technological cooperation. The Kinmen AI Computing Center can not only provide necessary computing resources and technical support to the mainland, but also create new economic growth points for Taiwan and enhance its influence in international technology competition.

However, the establishment and operation of this cooperation model will face many challenges, including technology transfer, intellectual property protection, political risks, etc. Therefore, this study will next explore how to balance the interests of all parties under this unique international science and technology and geopolitical structure, and promote cross-strait science and technology cooperation and economic prosperity in a peaceful and stable manner.

1.3 How does the Kinmen AI Computing Center help break through the blockade and promote cross-strait cooperation?

In the current international situation where the United States has imposed a technological blockade on AI chips, exploring effective breakthroughs has become a major issue in cross-strait scientific and technological cooperation. This paper will focus on the concept of Kinmen AI Computing Center, explore how it becomes a key hub for cross-strait cooperation, and specifically clarify the following research questions:

Feasibility of Kinmen AI Computing Center In view of the background of the international technology blockade, we will explore the feasibility of establishing an AI computing power center in Kinmen and its strategic position in cross-strait scientific and technological cooperation. Analyze whether this idea is practical and feasible, taking into account existing policies and legal frameworks, as well as the necessary technical infrastructure and human resources.
Mechanism to combat technological blockade Study how the Kinmen AI Computing Center can serve as a multi-functional platform to provide necessary AI computing resources to the mainland while ensuring compliance with cross-strait laws and regulations. Explore in detail how we can work together to avoid the negative impacts of lockdowns and promote technological development.
Strategies to promote cross-Strait cooperation Analyze how the Kinmen AI Computing Center can strengthen cross-strait cooperation in the field of science and technology, and study the strategies and measures needed to promote long-term cooperative relations. This includes how the two sides of the Taiwan Strait can effectively coordinate in protecting intellectual property rights and technology transfer.
Economic and political impacts Evaluate the impact of the establishment and operation of the Kinmen AI Computing Center on cross-strait economic relations, as well as the changes it may bring to the regional and even global technological innovation landscape. Explore whether such collaboration would have political implications and how these potential impacts could be managed.
Potential risks and challenges Identify risks and challenges that may be encountered in the process of establishing and maintaining the Kinmen AI Computing Center, including leakage of technical secrets, network security protection, and possible political sensitivity issues.

2. Literature review

2.1Related research and cases on AI technology blockade

In today’s global economic landscape, AI technology and the semiconductor industry have become the core of technological innovation. The competition between the United States and China in the field of AI chips has become increasingly fierce, leading to a series of technology blockades and export control measures. Chang and Yeh (2018) explored in their study the talent needs of Taiwanese companies as they face the Industry 4.0 transformation process, particularly emphasizing the importance of professional skills in the fields of AI and semiconductors. Cho (2023) analyzed the US-China decoupling model in the semiconductor and AI technology supply chains, which has had a profound impact on global technological innovation. Danilin (2022) regards the U.S.-China technological war as the beginning of a new geopolitical era, the impact of which may reshape the international order. Fang’s (2002) case study highlights the competitive advantages of Taiwan’s semiconductor industry, especially in terms of innovation and market responsiveness.
The research by Malkin and He (2023) analyzed the geoeconomics of the global semiconductor value chain and how U.S.-China technology competition affects the expansion of related value chains.

2.2 History and Current Situation of Cross-Strait Scientific and Technological Cooperation

The development history and current trends of cross-Strait scientific and technological cooperation are gradually formed in the long-term economic and cultural exchanges and the complex international political environment. From early cooperation in the field of information technology to now covering a wider range of high-tech fields, cross-strait scientific and technological cooperation has experienced continuous deepening and expansion.

Historical review Through a case study of cross-Strait cooperation in the LED industry, Tsai (2012) revealed that early cross-Strait scientific and technological cooperation was concentrated in specific high-tech fields, and emphasized that under the ECFA framework, both sides of the Strait should strengthen industrial cooperation to jointly enhance competitiveness. In addition, Hung’s (2009) study evaluated how Taiwan’s information technology industry promotes innovation and improves performance through cooperation with the mainland from the perspective of competition and cooperation.
Structural changes Entering the 21st century, cross-strait scientific and technological cooperation has undergone structural changes. Chen (2011) pointed out that investment by Taiwanese high-tech companies in the mainland not only promotes cross-strait economic cooperation, but also contributes to the formation and development of industrial clusters. In addition, the research by Yang, Sun, and Huang (2018) explored the possibility of cross-Strait energy cooperation under the new situation, and proposed that the energy field, especially offshore wind power, is a new area for cross-Strait cooperation.
Current challenges and status quo Although cross-Strait scientific and technological cooperation has a rich historical foundation and huge development potential, it currently faces many challenges. Clough (2001) analyzed from a political perspective the continued development of cross-Strait scientific and technological cooperation despite the political deadlock, highlighting the uniqueness and relative independence of the field of scientific and technological cooperation. Generally speaking, cross-strait scientific and technological cooperation has gradually expanded from early cooperation in information technology and electronic manufacturing to emerging fields such as energy, biotechnology, and artificial intelligence. Although faced with the challenges of changes in the international political and economic environment and fluctuations in cross-Strait relations, the potential for cross-Strait scientific and technological cooperation is still huge, providing new opportunities for future cooperation.

Reference, research methods

3.1 Research Design

This chapter aims to introduce the overall structure of this study, including the purpose, scope and problem statement of the study. Through an in-depth analysis of the Kinmen AI Computing Center concept, this study will explore the challenges faced by the center in promoting technological innovation, raising necessary funds, complying with relevant laws and regulations, and responding to changes in the cross-strait political environment. At the same time, the research will identify and propose potential strategies and solutions to promote the center’s stable and effective development amid uncertain cross-Strait relations.

Research purpose This study aims to gain an in-depth understanding of the feasibility of the Kinmen AI Computing Center concept and evaluate its implementation challenges and opportunities in the context of current international technology trends and cross-strait relations. In addition, the research also aims to provide a systematic analysis framework to promote the center to become an accelerator for cross-strait technological innovation and industrial development.
Research scope This research will focus on the following core areas:
- Technological innovation: Analyze the role and potential of Kinmen AI Computing Center in promoting cross-strait technological innovation, especially in the field of AI.
- Fundraising: Explores the challenges for centers raising funds in the current economic and political environment, and strategies for facing these challenges.
- Legal compliance: Evaluate the possible impact of cross-strait and international laws on the operation of the computing center, and how to ensure the legal operation of the center in a changing legal environment. Cross-Strait Political Relations: Study the impact of fluctuations in cross-Strait political relations on the stable operation of the Center, and seek strategies that can adapt to these fluctuations.
Problem statement Issues this study will explore include:

What role can the Kinmen AI Computing Center play in promoting cross-strait technological innovation and industrial development?
- In the complex cross-strait political environment, how will the center overcome challenges such as financing?
- How does the center ensure legal and compliant operations under the dynamically changing regulatory framework?
- In response to the uncertainty in cross-strait relations, how should the center plan and implement corresponding response strategies?
- In summary, this chapter will lay the foundation for the overall research and provide a clear direction and methodological basis for in-depth discussion in subsequent chapters.

3.2 Data collection method

In order to meet the diverse needs of this study for a comprehensive analysis of the Kinmen AI Computing Power Center, we adopted the following data collection methods:

Literature review In order to gain a deeper understanding of the background, development status and macro-environment of Kinmen AI Computing Center, literature review plays a fundamental role in this study. This includes but is not limited to:
- Government reports and planning documents: Collect official documents from both sides of the Taiwan Strait on science and technology plans, economic development strategies and AI policies.
- Academic articles and journals: Read extensively academic articles related to AI technology, computing center operations and management, cross-strait economic cooperation, etc., and summarize the latest research results and theoretical progress.
- News reports and industry analysis: Collect news reports from the fields of technology, economy and politics, as well as analysis reports from professional institutions on the development of technologies and markets related to AI computing power centers.
Technology-assisted analysis In view of the processing and analysis needs of large amounts of data in modern research, the author introduced an artificial intelligence auxiliary tool, namely ChatGPT-4, into the research process. This tool is mainly used for pre-processing of data. ChatGPT-4 is used to summarize and organize literature materials, including key information extraction and preliminary classification, to improve the efficiency and completeness of data collection.

3.3 Data analysis method

In order to improve the efficiency and quality of research, in this study, we used the artificial intelligence tool ChatGPT-4 as an auxiliary tool for writing papers. The application scope of ChatGPT-4 covers many aspects from the development of initial ideas, the integration of literature materials, to the formation of research proposals.

Preliminary analysis ChatGPT-4 provides us with insightful reference opinions and analysis perspectives in the preliminary analysis stage. Through dialogue with them, we were able to examine the problem from different perspectives and reveal potential research directions and analytical frameworks during the discussion.
Language expression and draft writing During the paper writing stage, ChatGPT-4 was used to improve the quality of language expression and the initial construction of the draft. Its capabilities include copy generation, writing style adjustment, and text proofreading and editing.
Data validation It is worth mentioning that all analyzes and recommendations generated using ChatGPT-4 are manually verified by the author. Ensure that the ideas presented are accurate, the data are reliable, and comply with academic research standards.

3.4 Ethical considerations

In the process of this research, we made full use of the large language model ChatGPT-4 as an auxiliary tool for writing papers. During this process, we paid special attention to and strictly adhered to several key ethical considerations to ensure that the research was conducted not only efficiently but also in compliance with academic ethical standards.

Transparency In the process of this research, we made full use of ChatGPT-4, an advanced artificial intelligence tool, to assist in the data collection, preliminary analysis, and paper writing process. In order to ensure the transparency of the research process, we hereby clearly state that we were assisted by large language models (LLM) in the writing process of this paper, especially in processing the large data set, generating preliminary analysis suggestions, and drafting the text of the paper. While using large language models, the author fully recognizes the responsibility as an author and researcher for the content of the final submitted paper. We ensure that all research results and conclusions are based on adequate data analysis, critical thinking, and scientific argumentation processes, and strive to make all relevant information and methods of this research as transparent and clear as possible.
Originality and Attribution In the process of using large language models to assist in the writing process, we strictly ensure the originality of the research results. Any content generated by the large language model will be manually reviewed and reconstructed to ensure that the content of the final paper is unique and attributed to the study authors. At the same time, we also pay attention to avoid directly quoting the generated content under inappropriate circumstances to protect intellectual property rights.
Responsibility In writing research using ChatGPT-4, the author recognizes that responsibility for the final research results and any related decisions remains with the researcher. This means that researchers need to critically evaluate and make appropriate choices for any analysis and suggestions provided by large language models to ensure the accuracy and reliability of the research results. Although we cannot specifically label every piece of content generated with the assistance of large language models, we promise that all research results we provide will undergo rigorous manual review and integration to ensure that they meet the standards and requirements of academic research.
Adhere to academic standards Throughout the entire research process, from data collection to paper writing, we adhere to academic ethics and standards. This includes, but is not limited to, accurate citation of references, thorough documentation of the research process, and objective presentation of the research results.

3.5 Research Limitations

In conducting this study, we faced several unavoidable limitations that may have affected the completeness and scalability of the findings. The following are the main study limitations we identified:

Accessibility of data collection Although we tried our best to collect a wide range of relevant literature and information, due to resource limitations and the non-publicity of some information, we were unable to obtain all potential information. In particular, some of the latest research results, dynamic updates of cross-strait policies, or the lack of specific technical details may have a certain impact on our research results.
Limitations of technical assistance Although ChatGPT-4, as an advanced artificial intelligence technology, has provided tremendous support for our research, its limitations in understanding complex problems, capturing cultural and regional details, and the interpretation of knowledge in certain professional fields may not be accurate enough. These factors limit the extent to which we can only rely on such technical tools for analysis and information collection.
Generalizability of research results This research mainly focuses on the establishment of the Kinmen AI Computing Power Center and its impact on cross-strait scientific and technological cooperation. Therefore, the scope and background of the research are specific to the region and timeliness. This also means that our research results may be limited by factors such as the state of cross-strait relations, technological development stage, and policy environment in a specific period, and may have limited generalizability and applicability to other regions or different periods.

Although we tried our best to overcome these limitations during the research process to improve the accuracy and reliability of the study, the existence of these research limitations reminds us that we need to be cautious when interpreting and applying the research results. Future research can expand and deepen by obtaining more comprehensive information, using more advanced technical tools, and exploring a wider range of application scenarios to overcome the limitations of the current research.

4. Policy, economic and technical background analysis

4.1 Specific measures of the US blockade policy and its impact on the mainland

Measures of the US blockade policy
- Export restrictions: In recent years, as technology competition has intensified, the U.S. government has implemented a series of export restrictions on AI chips and related technologies, aiming to restrict the delivery of these technologies to the mainland. These restrictions include updates to export rules for specific products and technologies, Entity List restrictions on specific businesses, and restrictions on investment. These measures have had a significant impact on mainland China’s scientific and technological progress and industrial development. According to the CSIS report (2022), the United States has updated its export rules for AI chips, restricting companies in specific regions, including mainland China, from purchasing American-made AI chips (Center for Strategic and International Studies, 2022). The updates to these rules are intended to prevent advanced U.S. technologies from being used to enhance China’s military capabilities or be used in unfair competitive practices. At the same time, The Diplomat (2024) reported that a series of export control measures first implemented in October 2022 were intended to restrict mainland China’s access to advanced AI chips manufactured in the United States (Cho, 2024). The goal of these control measures is to weaken the mainland’s ability to develop in the field of AI. In addition, according to Reuters reports (2024), the new rules aim to prevent the export of more advanced AI chips designed by Nvidia and other companies to the mainland (Reuters, 2024). These export restrictions have had a clear impact on the research, development and application promotion of AI technology. Therefore, the U.S. export restrictions have had a profound impact on the mainland’s technology industry and the global AI chip market. This not only affects the ability of mainland companies to obtain key technologies, but also has a broad impact on global technological innovation and cooperation.
- Entity List: In order to protect national security and economic interests, the U.S. government has implemented the so-called “Entity List” against specific foreign entities, organizations and individuals. The list includes entities deemed to potentially threaten U.S. national security or engage in unfair competition using U.S. technology. Companies placed on the list will be restricted from purchasing U.S.-made technology and equipment unless they have special permission. According to official information from the Bureau of Industry and Security (BIS) of the U.S. Department of Commerce, the “Entity List” clearly points out the licensing requirements faced by each company or individual listed (Bureau of Industry and Security, n.d.). Recently, according to the Federal Register, the U.S. Export Control Review Committee (ERC) decided to add several mainland companies to the “Entity List”, including Dennex Enterprises Limited, Exeya Co., Limited, etc. (Federal Register, 2024a; Federal Register, 2023). These new entities cover different fields from high-tech electronic products to trade and investment, reflecting the United States’ strict attitude towards technology export controls.
- Investment restrictions: In recent years, the U.S. government has taken a series of measures to restrict direct investment by U.S. companies in mainland technology companies, especially in sensitive technology fields. These restrictions are intended to prevent U.S. technology and funding from being used to enhance China’s military capabilities and technological competitiveness. Executive order restricts investment: According to the Council on Foreign Relations (Council on Foreign Relations, 2023), President Biden issued an executive order restricting U.S. direct investment in China in sensitive technology fields. This is intended to prevent these technologies from helping Beijing improve its military capabilities. New Procedure to Restrict Investment in China: Pursuant to the White House Case (White & Case, 2023), the Biden order establishes a new procedure to limit U.S. outward investment in specific technological fields in China. This includes investment restrictions in areas such as artificial intelligence and advanced semiconductors. Ban on certain high-tech investments: An executive order specifically targets three high-tech areas: artificial intelligence, advanced semiconductors, and quantum computing, banning new U.S. investments in China in these areas. (China Briefing, 2023)
Impact on Mainland China
- Challenges of technological development: In recent years, U.S. export controls and investment restrictions on mainland China have had a significant impact on mainland technology companies, especially in terms of acquiring key technologies and attracting international investment. Increased investment in R&D: According to the CSIS report, Western export control measures have caused a rebound among mainland companies that were included on the list, forcing them to increase investment in R&D (Center for Strategic & International Studies, n.d.). Although this can be seen as a positive push for independent innovation, it may face development bottlenecks in the short term due to the lack of mature technology. Impact of international trade relations: According to the Access Partnership report, US-China trade tensions have had a complex impact on the global supply chain and regulatory environment, making companies related to global supply chains face a more complex regulatory environment (Access Partnership, 2024). Security concerns and technological leadership: According to analysis by East Asia Forum, the United States’ export controls and investment restrictions on the mainland are designed to maintain technological leadership and address security concerns in key areas (East Asia Forum, 2024). This directly limits mainland technology companies’ access to key and advanced technologies and increases the difficulty of technological innovation. Concerns about the long-term impact on global trade: At the same time, U.S. companies are also concerned about overly broad restrictions on exports to China, fearing that it may have an impact on China’s long-term technological development and have negative consequences on global trade (Pillsbury Winthrop Shaw Pittman, n.d.).
- Market and supply chain adjustments: Facing the U.S. technological blockade and trade restrictions, mainland China has taken comprehensive measures to adjust its market strategy and restructure its supply chain. These strategies aim to reduce dependence on foreign technology while accelerating the development of domestic technological innovation and independent production capabilities.
- Promote domestic innovation: According to a report by the Center for International Strategic Studies (CSIS), mainland China is promoting a domestic innovation strategy and gradually reducing its reliance on imported technologies such as high-end chips (Kennedy & Johnson, 2023). This includes focused investment in research and development in key areas such as semiconductors and artificial intelligence. Expanding mainland’s internal production capabilities: In response to U.S. export controls, mainland China is accelerating the expansion of its internal semiconductor industry’s production capabilities. As research by Rhodium Group shows, China has significantly improved its traditional chip production capabilities in the past few years (Rosen, 2024). Diversify import sources: Faced with import restrictions on specific technologies and products, mainland China seeks to diversify import sources and reduce reliance on a single external market. This includes strengthening cooperation and trade in science and technology with countries in Europe, East Asia and other regions.
- Promotion of independent innovation: In recent years, in the face of U.S. blockade measures, mainland China has accelerated its pace of independent technological innovation, especially in key technology fields such as semiconductors and artificial intelligence (AI).
  - Semiconductor self-sufficiency strategy: According to a report by the Center for International Strategic Studies (CSIS), in the mid-2010s, mainland China had actively promoted a semiconductor self-sufficiency strategy. The key to this strategy is that in the face of external blockades and restrictions, mainland China increases investment and support in the domestic semiconductor industry to improve domestic production capacity and technological levels (Kennedy & Johnson, 2023).
  - Response to the U.S. technology blockade: The South China Morning Post (SCMP) reported that China’s response to the U.S. blockade strategy is to strengthen its capabilities in innovation and independent research and development, rather than choosing to isolate itself. A spokesman for the National People’s Congress stated that China will overcome U.S. technology and scientific sanctions through innovation and self-reliance (SCMP, 2024).
  - Development model in the AI field: According to a policy brief from the German Mercator Institute for China Research (MERICS), mainland China has adopted a nationwide large-scale computing infrastructure project and a comprehensive development strategy for AI development. This shows mainland China’s commitment to achieving technological independence in the field of AI (MERICS, n.d.).

Technology policy and development: According to a report by the National Bureau of Asian Research (NBR), in response to the challenge of U.S. restrictions, China has strengthened domestic efforts by encouraging innovation in the field of AI, increasing research and development funding (NBR, n.d.).

Through the analysis of the specific measures and impacts of the United States’ AI technology blockade policy in mainland China, this section provides an in-depth understanding of an important factor in the current global technology competition environment. These policies not only pose a direct challenge to mainland China’s technological development, but also prompt it to seek new market strategies and strengthen independent innovation capabilities, which will have a profound impact on the future development of cross-strait technological cooperation.

4.2 Taiwan’s technological and economic situation

Taiwan occupies an important position in the global semiconductor industry, especially in the field of AI chips. With the rapid development of global AI technology, Taiwanese companies have shown strong competitiveness in design, manufacturing and supply chain management.

Taiwan’s technological development Taiwan has made significant achievements in AI chip technology innovation, R&D investment, and talent cultivation. These efforts have supported its leadership in the global high-tech industry.
- AI chip technology innovation: As global technology competition intensifies, Taiwan’s innovation in chip technology is increasingly critical. The government has cooperated with industry to promote a series of innovation programs, such as the Taiwan Chip-based Industrial Innovation Program (Taiwan CbI), which focuses on upgrading academic research and teaching infrastructure and attracting global R&D talents (Taiwan Chip-based Industrial Innovation Program).
- R&D investment: Taiwan continues to increase R&D investment in the fields of AI and semiconductors. The success of Taiwan’s integrated circuit design industry is due to the government’s strong investment and support in technology research and development (EDN Asia, n.d.). In addition, while pursuing Moore’s Law, Taiwan has invested resources in cultivating talents to bring a new nano era to the semiconductor industry (Taiwan Semiconductor Manufacturing Company Limited, n.d.).
- Talent cultivation: In order to maintain its competitiveness in the global semiconductor industry, Taiwan has strengthened talent cultivation by upgrading its education and training system. This includes the establishment of high-level educational institutions and R&D facilities, as well as active interaction and cooperation with the international science and technology community (Runtime, n.d.).
Taiwan’s economic environment supports AI hardware and industrial chain Taiwan’s economic environment provides solid support for its AI hardware and the entire semiconductor industry chain. Taiwan plays a key role in the global AI supply chain, especially in the chip revolution, which has far-reaching influence.
- Key players in the global AI supply chain:
  - Taiwan’s leading position in AI chip technology innovation and production is due to its strong economic foundation and deepened investment in technology research and development, making it an indispensable link in the global semiconductor supply chain.
  - Under a highly specialized economic system, Taiwan not only has unique achievements in innovation, but also plays an important role in the global supply chain. According to CNBC, in a statement by the CEO of the Taiwan Stock Exchange, Taiwan has been proven to play a key role in the AI chip revolution and the process of the global semiconductor industry (CNBC, 2024).

In addition, Taiwan continues to increase R&D investment in promoting local and global AI chip technology innovation, and focuses on cultivating professionals in the technology industry. These efforts have strengthened Taiwan’s competitiveness in the international market and made significant contributions to global technological development.

- Margin of global chip manufacturing capacity:

Companies in Taiwan and South Korea have consolidated their dominant position in global chip manufacturing, especially in the production of AI chips. This advantage is not only technologically superior, but also has a profound impact on Taiwan’s economy.

Oxford Economics pointed out that since companies in Taiwan and South Korea control most of the world’s chip manufacturing capabilities, this has given Taiwan a competitive advantage in manufacturing AI chips and has a significant positive impact on its macroeconomics (Oxford Economics, n.d.). This impact is not only reflected in the promotion of internal industries, but also in its external contribution to the global supply chain and international market.

Taiwan’s economic development strategy, especially its manufacturing capabilities in the field of AI chips, highlights its central role in the global technology industry. In the current context of rapid changes in global technological development, Taiwan’s status has brought stable growth momentum and international business competitiveness to its economy.

- Industrial synergies:

Taiwan has demonstrated significant industrial synergy in all aspects of the AI chip industry chain. From front-end chip design to back-end foundry, packaging and testing, it has formed an efficient and integrated production network. This “one-stop” industry chain is unique to Taiwan and cannot be found in other countries such as the United States or Japan. The exact same model.

Taiwan Semiconductor Manufacturing Company (TSMC) is the world’s largest chip foundry, providing products for more than 70% of the world’s chip manufacturing needs, especially in the field of AI (Stimson Center, 2022). This industrial environment has made Taiwan an important hub for global AI hardware R&D and manufacturing. The industrial environment in Taiwan not only promotes close cooperation between industries, but also strengthens the integration of upstream and downstream supply chains. This synergy gives Taiwan an irreplaceable position in the global semiconductor market and ensures the smooth operation and technological progress of every link in the industrial chain.

The strengthening of Taiwan’s industrial chain and industrial synergy, especially in the field of AI chips, have become an important support point for its economic development. Taiwan’s industrial environment not only contributes significantly to the island’s economy, but also has a profound impact on the supply and innovation of the global AI hardware and semiconductor industries.

Wu, Kinmen AI computing power center concept

5.1 Concept and design principles of Kinmen AI Computing Center

With the promotion of the global digital economy, artificial intelligence (AI) technology has become a key force in promoting future technological development. In order to take the lead in this field, the idea of Kinmen AI Computing Center came into being, aiming to create a high-performance computing platform that integrates AI research, data processing and algorithm innovation.

Conceptual background The concept of the Kinmen AI Computing Power Center is based on the following core goals: to promote the research and development of AI technology, strengthen data processing capabilities, promote algorithm innovation, and lay a solid foundation for the commercialization and practical application of AI technology. The center is envisioned as a bridge connecting the scientific and technological circles on both sides of the Taiwan Strait, serving not only Taiwan, but also scientific research institutions and enterprises in the mainland and even around the world.
Design principles Innovation-driven: Kinmen AI Computing Center will adhere to the design principle of innovation as the core and promote the cutting-edge development of the AI field through continuous technology research and development and algorithm optimization. Sustainable development: During the construction and operation process, the center will attach great importance to environmental protection and energy efficiency, using green energy and efficient cooling systems to ensure a win-win situation between technological progress and environmental protection. Open cooperation: The center will establish an open cooperation mechanism to encourage cross-strait and international scientific research institutions, universities and enterprises to participate and share resources and results. Talent training: The center will cooperate with universities and colleges to set up graduate training and exchange programs to cultivate high-end talents in the AI field. Data security and privacy protection: While strengthening data processing capabilities, the center will strictly abide by laws and regulations on data security and privacy protection to ensure the legality and ethics of all research activities.
Anticipated impact The establishment of the Kinmen AI Computing Center is expected to have a profound impact on the development of AI technology on both sides of the Taiwan Strait and even around the world. It will not only accelerate the output of AI research results and promote technological innovation, but will also attract more scientific researchers and enterprises to devote themselves to the exploration and application of AI technology, further promoting economic development and social progress.

5.2 The impact of Kinmen’s geographical and political location on this concept

Kinmen, located at the forefront of the Taiwan Strait, has a unique geographical and political status. This location not only has a profound impact on its own development, but also has important strategic significance for the proposed Kinmen AI Computing Power Center.

The strategic value of geographical location Kinmen’s geographical location makes it an important bridge connecting mainland China and Taiwan. This geographical advantage provides the only strategic value for the AI computing power center. The center can make full use of this geographical advantage to become an important platform for cross-strait scientific and technological exchanges and cooperation, promoting the sharing of scientific and technological resources and the rapid transfer of scientific research results.
Challenges and opportunities in the political environment Kinmen’s special political status, located between mainland China and Taiwan, makes it play a subtle role in cross-strait relations. On the one hand, this location provides potential opportunities for cross-strait scientific and technological cooperation; on the other hand, it may also face challenges due to changes in the political atmosphere on both sides of the strait. The establishment of an AI computing power center requires careful assessment of this political environment and planning of flexible response strategies to ensure the smooth operation and long-term development of the center.
Possibility of strengthening cross-strait cooperation The establishment of the Kinmen AI Computing Center has special geographical and political significance and has the potential to become an important link in strengthening cross-strait scientific and technological cooperation. The successful establishment and operation of the center will promote broader consensus and cooperation between the two sides in talent exchange, data sharing and the formulation of technical standards, and inject positive scientific and technological elements into the stable development of cross-strait relations.

The realization of the concept of Kinmen AI Computing Power Center will be deeply affected by its geographical and political location. This location provides the center with unique strategic value and presents both opportunities and challenges. Prudent assessment and utilization of Kinmen’s geographical advantages and flexible response to changes in the political environment will be the key to realizing the central vision and promoting cross-strait scientific and technological cooperation.

5.3 The potential for cross-strait collaboration in talent, data and hardware

As global technology competition intensifies, the United States’ sanctions on mainland technology companies have highlighted the necessity of cross-strait technology cooperation. Mainland China has huge data resources and talent pool in the field of artificial intelligence (AI), but it is limited in access to computing power. Relatively speaking, Taiwan has advantages in the entire hardware manufacturing industry chain and is able to obtain high-end AI chips from companies such as Nvidia. This chapter will explore the potential for complementary cooperation between the two sides in AI hardware, data and technology development in this context, and how this cooperation can promote regional scientific and technological progress and economic development.

Talent and data advantages in mainland China

The mainland has invested heavily in talent training and data accumulation. The huge user base in the market provides valuable resources for the training of AI algorithms. Many universities and research institutions have trained a large number of technical talents in the AI field.

Taiwan’s entire industry chain of hardware manufacturing

Taiwan has a complete semiconductor industry chain from design and manufacturing to packaging and testing, and has not been affected by US sanctions. This means that Taiwan can obtain high-end AI chips from international companies such as Nvidia and retain the ability to manufacture advanced computing hardware. Taiwan’s independence and technological strength in the field of semiconductor manufacturing provide a substantial hardware foundation for cross-strait cooperation.

The synergistic potential of cross-strait cooperation

In the current context, cross-Strait collaborative cooperation is not only feasible but also obviously complementary. Taiwan can use its advantages in hardware manufacturing to provide the mainland with necessary AI computing capabilities. At the same time, the mainland’s data and talent resources can also promote Taiwan’s deepening of AI software and algorithm development. By establishing a cooperation mechanism and sharing technology and resources, both sides of the Taiwan Strait can jointly overcome existing technical limitations and promote the development and application of regional and even global AI technology.

Collaborative cooperation between the two sides in the field of AI has huge potential. By leveraging their respective superior resources and technological capabilities, the two sides can not only effectively respond to the challenges of international technological competition and political factors, but also jointly promote regional technological innovation and economic prosperity, setting a model for complementary cooperation. In the future, this cooperation model is expected to become an important way for regional and even global scientific and technological cooperation.

Challenges

In the process of implementing the Kinmen AI Computing Center, you may encounter the following challenges:

6.1 Data security and privacy protection

As data becomes the core asset of the modern economy, especially in the field of artificial intelligence, large-scale data collection and analysis have become indispensable. Against this background, the Kinmen AI Computing Center faces major challenges in ensuring data security and maintaining user privacy.

Complexity of Data Security

Data security is one of the top priorities in the operation of the AI computing center. With the expansion of data volume, especially the processing of sensitive data, centers must comply with strict data protection standards and implement advanced security technologies and protocols, such as encrypted transmission, access control and network isolation, to prevent data leakage, abuse or loss.

Necessity of privacy protection

The protection of user privacy is equally important. This is not only a technical issue, but also a legal and ethical issue. The AI computing power center needs to establish a transparent privacy protection policy to let users understand how their data is collected, used and shared. At the same time, the center also needs to comply with relevant privacy protection laws and regulations, such as the Personal Data Protection Act, and implement privacy impact assessment and privacy design principles.

Legal Compliance Framework

As emerging technologies develop, the relevant legal and regulatory frameworks continue to evolve. From the beginning of its construction, the Kinmen AI Computing Center must consider complying with international and cross-strait laws and regulations, including cross-border data transmission laws, and cooperate with legal professionals to ensure the legality and compliance of all operational activities.

Technological Innovation and Risk Management

In order to cope with these challenges, the Kinmen AI Computing Center needs to continuously carry out technological innovation and implement comprehensive risk management strategies. This includes using artificial intelligence to enhance security monitoring, developing new cybersecurity technologies, and establishing rapid response mechanisms to handle security incidents.

6.2 Raising capital investment

In the process of implementing the concept of Kinmen AI Computing Power Center, raising sufficient investment funds and ensuring the effective use of funds constitute an important and challenging task. To build an AI computing center that meets high standards, it not only requires huge initial investment to establish infrastructure, purchase advanced hardware equipment and develop software, but also requires continuous financial support for daily operations, technology upgrades and talent training.

Methods of raising funds

Government Funding and Subsidies: As an important project to promote technological development and regional economic growth, the Kinmen AI Computing Center can seek funding and subsidies from the governments on both sides of the Taiwan Strait. This includes direct government investment, technology development funds and financial support for specific technology projects.
Private Investment and Cooperation: In addition to government funds, the participation of private capital is also extremely important for raising funds. We can cooperate with private institutions such as technology companies, venture capital and private equity funds to attract their investment in AI computing center projects.
International Cooperation and Funding: In view of the international cooperation potential of the Kinmen AI Computing Power Center, cooperation with international technology companies, research institutions and transnational investment funds should be explored to obtain international financial support.

Effective use of investment

Clear investment priorities: When resources are limited, clear investment priorities are key to ensuring the effective use of funds. This includes prioritizing investment in infrastructure construction, research and development of key technologies and talent training.
Establish a strict financial management system: In order to ensure the effective use of funds, the Kinmen AI Computing Power Center needs to establish a strict financial management and supervision mechanism, including regular financial audits, strict control of project progress and budget, etc.
Promote industrial collaboration and ecological construction: Through the effective use of funds, we should not only promote the construction and development of the AI computing power center itself, but also promote the collaboration of surrounding industries and the construction of a technological ecosystem to achieve the multiplication effect of funds. Raising sufficient investment funds and ensuring their effective utilization are crucial to the implementation process of the Kinmen AI Computing Power Center. By raising funds through multiple channels and establishing a rigorous management and supervision mechanism, the Kinmen AI Computing Power Center is expected to achieve its ambitious construction goals and contribute to the development of AI on both sides of the Taiwan Strait and even around the world.

6.3 Uncertainty in cross-Strait relations

Facing the background of intensified global technological competition, the Kinmen AI Computing Center, as an important technological project located at the political and economic intersection of the two sides of the Taiwan Strait, will inevitably be directly affected by the fluctuations in cross-strait relations during its advancement. Therefore, how to ensure the stability and sustainable development of the center in a complex and unpredictable political environment has become an important key to determining the success or failure of the project.

The uncertainty in cross-Strait relations mainly comes from differences in political stances, changes in the international economic and trade environment, and the impact of the geopolitical landscape. Due to these uncertainties, Kinmen AI Computing Center faces a series of challenges such as investment risks, changes in partners, and technology transfer obstacles.

Strategy for Stable Development

Diversified investment sources: In order to reduce the risk of a single investment source, the center needs to seek diversified sources of funding and technical support, including international partners, the private sector and non-governmental organizations.
Strengthen the foundation of legitimacy: Solid planning in legal and compliance aspects, including business operations that comply with cross-strait and international laws, can maintain the center’s legitimacy and legal operations when political risks intensify.
Establish a flexible operating mechanism: Center operations need to be highly flexible and adaptable to quickly respond to changes in the political environment, including temporary adjustments to cooperation strategies or business focus.
Promote cross-Strait science and technology communication: Through active science and technology diplomacy and communication, strengthening mutual trust and understanding between the cross-Strait science and technology circles will help maintain the stability of cooperation amidst political fluctuations.
Risk Management and Emergency Preparedness: Develop comprehensive risk management plans and contingency plans to ensure assets, personnel and intellectual property are protected and normal operations can be quickly restored in the face of political crises.

During the implementation of the Kinmen AI Computing Center, the uncertainty of cross-strait relations undoubtedly brought severe challenges. However, through effective strategic planning and risk management, the center can maintain stable development amid fluctuations in cross-Strait political relations, and then play its important role in cross-Strait and even global technological and economic cooperation.

6.4 Compliance with laws and regulations

With the rapid development of AI technology and data use, relevant laws and regulations are constantly being updated and changed to adapt to new technological developments and social needs. For a large-scale project like the Kinmen AI Computing Center that covers multiple fields of application, how to ensure legal and compliant operations under different legal environments has become an ongoing and complex challenge.

Uncertainty of the legal environment

Regional Differences in Laws and Regulations: Due to differences in cross-strait and international laws and regulations in data protection, privacy, intellectual property, etc., the AI computing power center needs to accurately grasp and comply with the regulatory requirements of different legal jurisdictions, which places high demands on its legal strategy and business operations.
Technological innovation and regulatory lag: The speed of technological innovation often exceeds the update speed of laws and regulations, resulting in unclear legal guidance in areas such as emerging technology applications and cross-border data flows, increasing operational risks.

Legal and Compliance Operation Strategy

Establish a professional legal team: Arrange a professional legal advisory team to continuously monitor and analyze cross-strait and international laws to guide the center’s legal strategies and business decisions.
Strengthen compliance training and awareness: Regularly conduct training on laws, regulations and compliance operations for the center’s management and employees to enhance the legal awareness and compliance level of all employees.
Actively participate in the discussion of laws and regulations and the formulation of standards: By participating in the discussions of relevant laws and regulations and the formulation of international standards, we can not only keep abreast of regulatory trends, but also strive for more favorable operating conditions for the center.
Establish risk management and response mechanism: Develop a comprehensive risk management and response mechanism including legal risk assessment, preventive measures and contingency plans to deal with the possible impact of changes in laws and regulations.

Facing the uncertainty and challenges of the legal environment, Kinmen AI Computing Center must adopt a proactive attitude, establish a professional legal team, strengthen compliance training, participate in regulatory discussions and establish risk response mechanisms to ensure legal and compliant operations in a rapidly changing legal environment. Through the implementation of these strategies, the Kinmen AI Computing Center will be able to steadily promote technological innovation and business development while ensuring legal compliance.

7. Conclusion and suggestions

This study conducted an in-depth discussion from multiple dimensions including the proposal of the Kinmen AI Computing Power Center concept, design principles, and challenges that may be encountered during the implementation process. Research has found that the Kinmen AI Computing Power Center, as an innovative scientific and technological development project, not only plays an important role in promoting regional scientific and technological progress and improving economic development, but also plays a bridge role in cross-strait scientific and technological cooperation and exchanges, providing new ideas and platforms for the stable development of cross-strait relations.

The complementarity of cross-strait cooperation in talent, data and technology provides a solid foundation for the successful implementation of the Kinmen AI Computing Center. By deepening cross-strait cooperation in the field of AI, it will not only promote technological innovation and knowledge sharing, but also help build closer economic ties and cultural exchanges, thereby enhancing mutual trust and promoting peaceful development.

7.1 Specific recommendations and future research directions

Based on the findings of this study, the following suggestions are made for the future development of Kinmen AI Computing Center and cross-strait cooperation:

Strengthen policy support and investment

It is recommended that governments on both sides of the Taiwan Strait provide more policy support and financial investment, lower the threshold and risks of project implementation, and create a more friendly investment environment.

Establish an ongoing talent exchange program

Both sides of the Taiwan Strait should jointly formulate and implement talent training and exchange plans to provide sufficient scientific research and technical talents for AI computing power centers.

On the premise of ensuring data security and personal privacy, promote the sharing of cross-strait data resources, and participate in the formulation of relevant technical standards to improve technical interoperability and compatibility.

Strengthen the docking and cooperation of laws and regulations

Both sides of the Taiwan Strait should strengthen docking and cooperation in science and technology laws and regulations to provide strong guarantee for the legal operation of AI computing power centers.

Future Research Directions

It is recommended that future research can further explore the potential for cooperation between the two sides in specific AI application fields (such as smart medical care, environmental monitoring, etc.), and propose specific solutions to overcome challenges in technology transfer and intellectual property protection.

7.2 Conclusion

The concept of the Kinmen AI Computing Power Center is not only of great significance to strengthening cross-Strait scientific and technological cooperation and promoting regional economic development, but also plays an indispensable role in promoting the peaceful and stable development of cross-Strait relations. By implementing the above recommendations and continuing to explore new cooperation models and opportunities, both sides of the Taiwan Straits can jointly meet the challenges brought about by technological innovation and work together to create a more prosperous future.

References

[1] Chang, Y. H. & Yeh, Y. J. Y. (2018). Industry 4.0 and the need for talent: A multiple case study of Taiwan’s companies. International Journal of Product Development, Inderscience Publishers. [2] Cho, E. K. (2023). US-China decoupling patterns in supply chains and ecosystems for semiconductors and AI technologies. Korea Institute for Industrial Economics & Trade. [3] Danilin, I. V. (2022). The US-China tech war: A dawn of new geopolitics? In Technological Innovation and Security. [4] Fang, J. J. M. (2002). The competitive advantage: A case study of Taiwan’s semiconductor industry. Retrieved from MIT Dspace. [5] Malkin, A. & He, T. (2023). The geoeconomics of global semiconductor value chains: Extraterritoriality and the US-China technology rivalry. Review of International Political Economy, 1–23. Taylor & Francis. [6] Tsai, C. W. (2012). Cross-strait cooperation of information technology: A case study of LED industry. ICSSSM12. [7] Hung, J. H. (2009). Enhancing innovation and performance through cross-strait competition and cooperation: The case of Taiwan’s information technology industry. ProQuest Dissertations Publishing. [8] Chen, H. N. (2011). Political economy of cross-strait investments: The Taiwanese high-tech industry as an example. Seoul Journal of Economics. Retrieved from [9] Yang, F., Sun, C., & Huang, G. (2018). Study on cross-strait energy cooperation under the new circumstance. Journal of Cleaner Production, 187, 591-599. [10] Clough, R. N. (2001). Growing cross-strait cooperation despite political impasse. American Foreign Policy Interests, 23(5), 233-244. [11] Center for Strategic and International Studies. (2022). Updated October 7 semiconductor export controls. Retrieved from [12] Cho, E. K. (2024). AI chips for China face additional US restrictions. The Diplomat. Retrieved from [13] Reuters. (2024). U.S. updates export curbs on AI chips and tools to China. Retrieved from [14] Bureau of Industry and Security. (n.d.). Entity list. U.S. Department of Commerce. Retrieved from [15] Federal Register. (2024a). Additions of entities to the entity list. U.S. Government Publishing Office. Retrieved from [16] Federal Register. (2023). Addition of entities to the entity list. U.S. Government Publishing Office. Retrieved from [17] Council on Foreign Relations. (2023). President Biden has banned some U.S. investment in China: Here’s what to know. Retrieved from [18] White & Case. (2023). President Biden orders establishment of new program to restrict US outbound investment in certain tech sectors in China. Retrieved from [19] China Briefing. (2023). US ban on new investments in targeted high-tech sectors. Retrieved from [20] Center for Strategic & International Studies. (n.d.). Balancing the ledger: Export controls on U.S. chip technology to China. Retrieved from [21] Access Partnership. (2024). Tech policy trends 2024: The US-China rivalry’s impact on global trade. Retrieved from [22] East Asia Forum. (2024). Examining US export controls against China. Retrieved from [23] Pillsbury Winthrop Shaw Pittman. (n.d.). Recent updates on foreign investment restrictions and export controls. Retrieved from [24] Kennedy, S., & Johnson, C. K. (2023). China’s new strategy for waging the microchip tech war. Center for Strategic & International Studies. Retrieved from [25] Rosen, D. H. (2024). US Pathways to Regulating China-Sourced Legacy Chips. Rhodium Group. Retrieved from [26] Kennedy, S., & Johnson, C. K. (2023). China’s new strategy for waging the microchip tech war. Center for Strategic & International Studies. Retrieved from [27] SCMP. (2024). China’s two sessions 2024: top legislature vows to overcome US tech and science sanctions. Retrieved from [28] MERICS. (n.d.). China’s AI development model in an era of technological deglobalization. Retrieved from [29] NBR. (n.d.). China’s tech policies and development: Responding to great power competition. Retrieved from [30] Taiwan Chip-based Industrial Innovation Program. (n.d.). Retrieved from [31] EDN Asia. (n.d.). Taiwan sets sight on cultivating IC design talent. Retrieved from [32] Taiwan Semiconductor Manufacturing Company Limited. (n.d.). Innovation Management. Retrieved from [33] Runtime. (n.d.). Chip companies worried about war wanted a new Taiwan – now they’re investing in Israel. Retrieved from [34] CNBC. (2024). Taiwan plays a ‘very crucial role’ in AI supply chain, says TWSE chief. Retrieved from [35] Oxford Economics. (n.d.). The macro impact of the AI boom in Taiwan and South Korea. Retrieved from [36] Stimson Center. (2022). Semiconductors and Taiwan’s “Silicon Shield”. Retrieved from

Research on the behavioral patterns of mainland students coming to Taiwan - analysis based on questionnaire survey

Thu, 28 Nov 2019 00:00:00 +0000

Seminar information

Conference Name: 2019 “National Development and Mainland China Studies” Master’s Academic Thesis Seminar

Meeting Time: November 28, 2019

Meeting location: Conference Room 1, Xiaofeng Memorial Hall, Chinese Culture University

Sponsor: Institute of National Development and Mainland China, Academy of Social Sciences, Chinese Culture University

Conference Manual and Proceedings: Conference Manual and Proceedings

Research Summary

Research background

Since Taiwan opened its doors to mainland students to study in Taiwan in 2011, a large number of mainland students and exchange students have come to Taiwan to receive the baptism of multiculturalism.

Problem awareness

Due to a series of “Lennon Wall” conflicts between Hong Kong students and mainland students in Taiwan since September 2019. The author is curious about whether the Taiwanese government’s policy discrimination of “three restrictions and six no’s” and lack of health insurance, as well as the cultural discrimination of Taiwanese people on accents, social rules, etc., through living in Taiwan and being exposed to Taiwan’s society, culture, economy, and politics, have they increased their identification with the mainland? Or is it because of the greater knowledge and experience in Taiwan that the mainland students have a negative attitude towards the mainland? Will Lu Sheng hide himself due to the cultural shock in Taiwan, or will he continue to be willing to express his opinions?

Research purpose

In this context, the study selected mainland students as subjects. Due to the differences in social systems, will the behavior patterns of mainland students in Taiwan be different due to their in-depth contact with Taiwan? Will their behavior patterns and interactions with people be different? Will different conclusions be drawn due to different factors such as gender, age, place of residence, the exchange student’s original school, the location of the school in Taiwan, or whether it is national or private?

Research methods

This article adopted the online questionnaire survey method and finally obtained 127 valid samples. Use statistical analysis, descriptive analysis, and cross analysis methods.

Research findings

It is hoped that through this research survey, it will be found that mainland students have a negative evaluation of Taiwan on the political level, and a positive evaluation of the social division, friendliness and value of coming to Taiwan to study.

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Publications | Dylan Chiang

Promoting Integration through Competition: Research on the Model and Path of Constructing Cross-Strait Youth AI Innovation Competition

1. Origin of the problem and research significance

2. Development status and practical difficulties of cross-strait youth AI competition

3. Conceptual model of “promoting financing through competition”: issue guidance, collaborative co-creation and value empowerment

4. Specific case: “Straits Youth AI Integration Innovation Competition” program design

5. Path suggestions for cross-strait youth AI innovation competitions

Algorithmic power gap and potential dilemmas in rural revitalization: theoretical analysis and situational deduction

Summary

1. The risk of AI bias in rural resource allocation and public services

2. The operation and inequality of algorithmic power in rural context

3. Potential impact on rural revitalization goals

(1) Algorithmic bias erodes the goals of social equity and inclusive development

(2) The algorithmic power gap further marginalizes disadvantaged rural communities

4. Conclusion and Policy Implications

(1) Summary of main arguments

(2) Policy Enlightenment

5. Research Prospects

References

Research on the usage behavior of generative artificial intelligence: taking a legislative assistant as an example

Summary

Reference errata (Errata, 2026-06-12)

English Title

Chapter 1 Introduction

1.1 Research background and motivation

1.2 Research purposes and questions

1.3 Research scope and limitations

Chapter 2 Literature Discussion

2.1 Research related to generative artificial intelligence

2.2 Technology Application in Political Work

2.3 Using behavioral theory

Chapter 3 Research Methods

3.1 Research design

3.2 Research objects and samples

3.3 Data collection method

3.4 Data analysis methods

Chapter 4 Research Results

4.1 Descriptive statistical analysis

4.2 Using behavior analysis

4.3 Analysis of influencing factors

Chapter 5 Discussion and Suggestions

5.1 Discussion of Research Findings

5.2 Practical suggestions

5.3 Research limitations and future research directions

Chapter 6 Conclusion

References

Appendix

From technological innovation to cultural inheritance: the role of AIGC technology in rural revitalization and cultural tourism products

Chapter 1 Introduction

1. Research background

2. Research purpose

3. Research scope and methodology

Chapter 2 AIGC Technology Analysis and Transnational Development Trends

1. Overview of AIGC technology

2. Development trends of AIGC technology in China and the United States

Chapter 3 Application of AIGC technology in local cultural tourism products

1. Application cases of large language models (LLMs)

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1. The meaning of automatically generated travel guide

2. Advantages of real-time updates

3. Personalization and interactivity

(2) Real-time translation and language conversion: breaking language barriers

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2. Diversity of language conversion

3. Enhance cultural exchanges

(3) Cultural Heritage Narrative: Revitalizing History and Connecting the Future

1. Innovation in digital narrative

2. Enhancement of education and display functions

3. Provision of interactive experience

4. Preservation of cultural diversity

2. Comprehensive application cases of Vincentian diagrams and Tusheng diagrams

(1) Cultural and creative product design: the combination of creative photography and drawing technology

Case Study: Traditional and Personalized Product Innovation

1. Combination of creative photography and dragon image

2. Diversified design of cultural and creative products

3. The connection between culture and market

(2) Cultural and creative product design: the combination of Wenshengtu technology and aboriginal culture

Case Study: Innovative Design of Aboriginal Girl Figures

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2. Creative development of blind box products