[Purpose/Significance] The rapid development of artificial intelligence (AI) technology has reshaped the demand for data governance that is compliant, comprehensive, and refined. The European Union (EU) has proactively established a benchmark framework for AI data governance through targeted policy measures. However, there is a lack of systematic analysis on the policy layout and governance characteristics of AI data governance in the EU, both domestically and internationally. This paper focuses on the AI data governance policies in the EU, aiming to reveal the development process, policy layout, and governance characteristics of AI data governance in the region, providing valuable insights and references for advancing the global paradigm of AI data governance. [Method/Process] This paper systematically collects core AI data governance policy documents from 10 EU member states and the United Kingdom through multiple channels. By manually reviewing and selecting policy units related to "AI data governance," the paper traces the development process and uses a three-dimensional analytical framework - governance goals, governance bodies, and governance tools - to reveal the policy layout and governance characteristics of AI data governance in the EU. [Results/Conclusions] The study found that AI data governance in the EU has transitioned from soft law guidance to hard law regulation, gradually establishing three key governance goals: data ethics protection, data security defense, and data value release. Through the establishment of a multi-level legislative system and a coordinated execution framework, the EU focuses on regulatory constraints, procedural norms, AI system element support, and data ecosystem construction, demonstrating comprehensive governance capabilities. First, the EU has constructed a consensus framework for data governance through unified norms, centrally coordinating the diverse needs of member states during policy implementation, ensuring high consistency of governance rules across the EU. Second, the EU's policy design strikes a balance between rule uniformity and national autonomy, allowing member states to adjust policies flexibly according to their unique data cultures and industrial structures, fostering better localized governance. Third, the EU's governance model achieves a dynamic balance between "strong regulation" and "promoting development," ensuring the protection of citizens' rights through stringent ethical and risk prevention measures, while fostering innovation by releasing data value and driving AI industry growth. This paper provides a systematic analysis of the layout and characteristics of AI data governance in the EU. Future research could compare the EU framework with AI data governance policies in other major economies, such as the United States and China, to identify their respective strengths and weaknesses.
[Purpose/Significance] The rapid advancement of artificial intelligence (AI) has fundamentally transformed academic research and information services. This makes AI literacy education a critical part of the strategy for academic libraries. As AI technologies become integrated into various aspects of scholarly activities, including literature searches, data analysis, academic writing and publishing, libraries must expand their traditional information literacy programs to include comprehensive AI competencies. This study focuses on analyzing AI literacy education practices in Nordic academic libraries, which are recognized for their progressive approaches to digital education and technology integration. By examining these international exemplars, the research aims to provide valuable references for academic libraries in China. The findings will help libraries develop systematic approaches to equip faculty and students with both technical AI skills and critical understanding of AI's ethical implications, ultimately supporting the cultivation of future-ready talents in the digital era. [Method/Process] This research employed a web-based survey methodology to investigate AI literacy education programs in 23 academic libraries across Nordic countries (Denmark, Finland, Norway, and Sweden). The study systematically analyzed four key dimensions of these programs: educational stakeholders (including libraries, faculty, and IT departments), target audiences (undergraduates, graduate students, researchers, and faculty), educational content (covering both technical skills and ethical considerations), and instructional formats (such as workshops, courses, and online modules). The selection of Nordic libraries as case studies was based on their established reputation in digital literacy education and early adoption of AI-related services. Data collection focused on publicly available information about each library's AI education initiatives. The analysis particularly emphasized how these libraries integrated AI literacy within their existing information literacy frameworks while addressing the specific needs of different user groups. [Results/Conclusions] The investigation revealed several effective practices in AI literacy education. First, successful programs typically involved collaboration among multiple stakeholders, with libraries working closely with academic departments, IT services, and sometimes external partners to develop comprehensive curricula. Second, the content was carefully designed to address different competency levels, from basic AI awareness for undergraduates to advanced applications for researchers. Third, most programs balanced technical instruction with critical discussions about ethical challenges such as algorithmic bias and data privacy. Fourth, diverse delivery methods were employed, including hands-on workshops, credit-bearing courses, and self-paced online modules, allowing for flexibility in learning. For Chinese academic libraries seeking to enhance their AI literacy offerings, these findings suggest several practical recommendations: establishing cross-departmental collaboration mechanisms to pool expertise and resources; developing tiered educational content that caters to users with varying needs and backgrounds; incorporating both technical training and ethical discussions into the curriculum; and adopting flexible teaching formats to maximize accessibility. Future development should focus on creating localized AI literacy frameworks that consider China's unique educational context and technological landscape, while maintaining international perspectives through continued dialogue with global peers.
[Purpose/Significance] AI for Science (AI4S) refers to the thorough integration of artificial intelligence (AI) into scientific, technological, and engineering research. It is driving a fundamental transformation in the way science is conducted by automating knowledge generation and enabling full-spectrum intelligence across the entire research lifecycle, and fostering interdisciplinary convergence Widely regarded as the fifth paradigm of scientific inquiry, following experimental, theoretical, simulation-based, and data-intensive research, AI4S heralds a new era of knowledge discovery. AI4S is an emerging paradigm that presents new challenges and higher demands for libraries' AI literacy education and AI services. Leading North American research universities, such as Harvard University, Dartmouth College, and the University of Toronto, have already leveraged their libraries to provide AI guides services supporting scientific research. Adopting the novel lens of AI4S-oriented services, this paper offers a systematic analysis of how North American research libraries have developed their AI4S guides, with the aim of providing transferable insights and practical references for research university libraries in China as they design research-support services under the AI4S paradigm. [Method/Process] This study employs Web survey and content analysis methods, using member libraries of the Association of Research Libraries (ARL) in North America as samples, to analyze the characteristics of AI4S guides in research libraries. It examines the key components of AI guides from three perspectives: AI cognition, AI tools, and academic usage norm. [Results/Conclusions] The survey was conducted from 1 December 2024 to31 March 2025. Among all ARL libraries, 97 were found to offer research-oriented AI guides: 85 from the United States and 12 from Canada. The analysis revealed the three hallmarks of the AI4S guides developed by the ARL members: 1) LibGuides serve as the primary delivery mechanism for AI4S guidance, 2) the guides are deeply integrated with AI-literacy instruction, 3) generic research guides coexist with and complement discipline-specific guides. These AI4S guides provide comprehensive support for researchers' capacity-building, covering the role of AI in research, informed selection of AI tools, and responsible use of AI technologies. Recommendations for AI tools occupy a central place in AI4S services; across the libraries, the endorsed tools fall into three categories: institution-developed tools, institution-approved tools, and third-party tools. Based on the findings, the paper proposes recommendations for research university libraries in China to develop and enhance AI guides under the AI4S paradigm, including strengthening policy interpretation, prioritizing data security, and promoting cross-departmental collaboration. This study still has the following limitations: 1) it is based on an analysis of the overall situation, and the analysis of typical cases of AI4S services is insufficient, 2) it did not conduct a comparative analysis of AI4S guides and services between domestic and international libraries.
[Purpose/Significance] In the digital era, information literacy has evolved from an academic skill into a fundamental competency that is essential for civic participation and lifelong learning. Traditional information literacy education in digital libraries is faced with significant challenges including the need for standardized content delivery, limited interactivity, high development costs, and insufficient user engagement. The rapid advancement of generative artificial intelligence (GenAI) technologies presents an unprecedented opportunity to transform information literacy education by leveraging powerful capabilities in natural language processing, personalized interaction, and content generation. This study represents a pioneering systematic exploration of how GenAI can be strategically integrated into digital library information literacy education, It addresses a critical gap in existing research, which primarily focuses on general educational applications rather than library-specific contexts. The research strengthens the theoretical basis of AI-enhanced library education and offers practical advice to institutions adopting innovative educational technologies while upholding quality and ethical standards. [Method/Process] This study employs a comprehensive mixed-method approach combining systematic literature review, theoretical analysis, and conceptual framework development. The methodology is grounded in well-established information literacy frameworks, particularly the ACRL Framework, which provides a foundation for breaking down information literacy education into five key components: information need identification, retrieval strategy development, resource evaluation, information management, and ethics education. A four-dimensional challenge analysis framework was constructed encompassing content quality and credibility, pedagogical methods and learning outcomes, ethics and social equity, and operational considerations. The research synthesizes evidence from emerging AI-enhanced education practices, preliminary library applications, and educational technology literature to develop comprehensive application pathways and strategic responses. [Results/Conclusions] The research identifies specific GenAI integration pathways across the complete information literacy process. Applications include intelligent dialogue guidance for need identification, simulated training environments for retrieval skills, controlled assessment materials for evaluation practice, and interactive ethical scenario simulations. Four primary challenge categories are revealed: content quality issues including AI hallucination and embedded biases; pedagogical challenges such as over-dependence risks and assessment complexity; ethical concerns encompassing data privacy and algorithmic discrimination; and operational challenges including implementation costs and staff capability requirements. Strategic responses include human-AI collaborative review mechanisms, process-oriented task design emphasizing critical thinking, transparent ethical governance frameworks, and comprehensive staff development initiatives. The study emphasizes librarian role transformation toward learning facilitators, AI literacy educators, and ethics advocates. Despite contributions, limitations include reliance on theoretical analysis rather than empirical validation and insufficient attention to user group heterogeneity. To ensure equitable and effective AI-enhanced information literacy education, future research should prioritize empirical outcome studies, case studies of pioneering implementations, and development of library-specific AI tools.
[Purpose/Significance] As digital technology continues to reshape the preservation and utilization of cultural heritage, the study of the value co-creation of cultural heritage data resource has gained increasing importance. The growing significance of cultural heritage data, coupled with advancements in digital tools such as big data, artificial intelligence, and virtual reality, require a deeper understanding of the collaborative processes that create value. This research focuses on the value co-creation mechanism of cultural heritage data resources, aiming to offer new perspectives on how diverse stakeholders, including cultural heritage institutions, digital technology providers, and the public, interact dynamically across different stages of data resource management. By proposing a three-dimensional analysis framework based on "stages-subjects-scenarios," this study not only enhances the understanding of the co-creation process, but also contributes to the academic field by exploring the role of different stakeholders in different contexts. The innovation lies in the application of this framework to analyze the specific mechanisms of value co-creation, highlighting the different involvement levels of stakeholders in various stages of data management and usage. The study provides practical implications for improving the management and utilization of cultural heritage data resources, particularly in the context of fostering interdisciplinary collaboration and community engagement. [Method/Process] The study takes an integrated approach, combining case analysis, stakeholder theory, and qualitative research methods, with a particular focus on expert interviews and case study reviews. Through a systematic review of both domestic and international examples, the research explores how different phases of data management - such as data collection, integration, sharing, and application - unfold in practice. The case studies were selected using a multi-source approach, which includes authoritative recommendations, literature reviews, and online surveys to ensure a diverse set of representative projects. We analyzed each case to identify the key stages and stakeholders, and how they interact within specific application scenarios. The theoretical foundation is grounded in stakeholder theory and value co-creation frameworks, while empirical evidence was drawn from ongoing projects in the digital humanities and cultural heritage fields. Using this combination of theoretical and empirical sources, the research developed a thorough understanding of how value co-creation mechanisms evolve and manifest in the context of cultural heritage data management contexts. [Results/Conclusions] The research reveals that the value co-creation of cultural heritage data resources involves multiple stakeholders, each contributing differently at various stages of the process. The identified stages include data collection, integration, sharing, application, and dissemination, each with distinct stakeholder involvement. Key stakeholders include cultural heritage institutions, digital technology providers, content creators, government bodies, and the public, each playing a critical role at different phases. For instance, cultural heritage institutions are central during the data collection and preservation stages, while content creators and developers take a more prominent role during the application and innovation stages. The research also identifies that stakeholder participation varies across different application scenarios, such as digital exhibitions, educational projects, and creative industries. The study concludes that achieving effective value co-creation requires a flexible, collaborative approach, tailored to the specific needs of each stage and scenario. Recommendations for future practice include improving collaboration between stakeholders, encouraging public participation, and establishing clearer frameworks for data governance and intellectual property rights.
[Purpose/Significance] Studying whether the development of the digital economy can boost rural household consumption is related to expanding rural consumption potential in the digital age. This is significant for leading the country's overall economic development and overcoming obstacles that restrict the growth of domestic demand. The research topic has been expanded to include research related to the digital economy. The contribution of this paper lies in the following aspects. First, few scholars currently consider refining the types of consumption for the research between the two. This paper starts from the heterogeneous consumption structure to explore the differences in the impact of the "broadband rural" policy on the consumption structure of rural households creating diversified consumption needs and experiences. This promotes new consumption, and further taps the consumption potential of rural households. Second, previous scholars primarily focused on macro-city data, while this paper uses micro-level data from the China Family Panel Studies (CFPS) from 2010 to 2022 to extend the identification period of the effects of policy dynamics. Based on the level of farmers, this paper examines the differential impact of the digital economy on the individual consumption behavior of farmers. Third, it introduces family endowment into the influence mechanism of digital economy on farmers' household consumption, discusses the adjustment mechanism of endowment difference in policy influence, and supplements the research perspective of previous scholars. [Method/Process] Based on the data of China Family Panel Studies (CFPS) from 2010 to 2022, this paper constructs seven periods of unbalanced panel data, takes the "broadband rural" policy as a quasi-natural experiment, adopts the methods of difference-in-differences, triple difference method and PSM-DID, and combines Keynesian absolute income hypothesis, information asymmetry theory and precautionary savings theory to evaluate the impact of digital economy on farmers' household consumption. [Results/Conclusions] As a result, the digital economy has significantly promoted the consumption of rural households, but it is not significant in the impact of enjoyment consumption. Combined with mechanism analysis and heterogeneity analysis, family endowment has a significant moderating effect, and the impact of digital economy has group differences. Based on this, this paper puts forward some countermeasures and suggestions to promote the dividend sinking of digital economy development, focus on the support of heterogeneous groups, and reasonably advocate new consumption. It can be seen that the impact of digital economy on the consumption of peasant households still needs to be further explored, which is of great significance to realize the domestic cycle and international double cycle. However, this is difficult to achieve due to data limitations and the need for long-term tracking. Therefore, in the future, the effect analysis of the "broadband village" policy will be extended to analyze its long-term impact on the consumption of peasant households.
[Purpose/Significance] This study addresses the "motivation black box" problem. By integrating achievement goal theory and technology acceptance models, it aims to construct a four-dimensional "motivation-identity-cognition-engagement" theoretical framework to analyze the driving mechanisms underlying AI teaching assistant usage behavior. [Method/Process] A questionnaire survey was utilized in this study. The Chaoxing Learning platform served as the research context, and college students who use AI teaching assistants constitute the research subjects. The chain mediating effect between technical identity recognition and technical acceptance was tested using the structural equation modeling (SEM). The significance of the pathways was verified via the Bootstrap sampling method. Data analysis was performed using SPSS 26.0 and Smart PLS 3.3.9 software. [Results/Conclusions] Key findings reveal that within the learning environment integrating Chaoxing's online courses with AI teaching assistants, achievement goal orientations demonstrated significant divergence, with mastery-approach goals (MAP) emerging as the sole significant driver - other goal orientations showed no statistically reliable predictive effects. Crucially, MAP significantly promoted dependent (β=0.308), critical (β=0.262), and exploratory (β=0.244) usage behaviors through the "technology identity recognition → technology acceptance" chain-mediation pathway. Furthermore, technology identity recognition exhibited dual mediation dominance in behavior formation, as this chain-mediation pathway accounted for more than 50% of total effects across all three usage behaviors, particularly for dependent and exploratory usage. Notably, technology identity recognition demonstrated the strongest mediation effect specifically on dependent behaviors (β=0.418). Further analysis indicates MAP's total effect on technology identity recognition substantially exceeded its direct effect on technology acceptance. This critical finding aligns with Deci and Ryan's self-determination theory, confirming that intrinsic motivation (exemplified by MAP) facilitates deeper skill internalization. Specifically, students focused on competence development showed greater tendency to integrate AI skills into their self-concept (e.g., perceiving themselves as "technology-proficient learners") rather than viewing them merely as external tools - a mechanism that empirically explains why traditional technical training that emphasizes operational skills often fails to foster sustained usage. Most significantly, this research provides important implications for educators in guiding students' use of AI teaching assistants: they should prioritize cultivating students' mastery-approach goals (MAP) through instructional design that strengthens students' pursuit of knowledge. Such an approach enhances the effectiveness of AI tools in teaching while simultaneously offering direction for the Chaoxing Learning Platform to optimize its AI teaching assistant features. Specifically, the platform should enhance personalized learning support tailored to the needs of MAP-oriented users, thereby better aligning with students' intrinsic learning motivations.
