[Purpose/Significance] In recent years, large language models (LLMs) have achieved revolutionary breakthroughs in semantic understanding and generation capabilities through massive text pre-training. This has injected brand-new impetus into the field of knowledge engineering. As a structured knowledge carrier, the knowledge graph has unique advantages in integrating heterogeneous data from multiple sources and constructing an industrial knowledge system. In the context of a paradigm shift in the field of knowledge engineering driven by the emergence of open-source LLMs such as DeepSeek, this study proposes a cost-effective method for constructing domain knowledge graphs based on DeepSeek. We aim to address the limitations of traditional domain knowledge graphs, such as high dependence on expert rules, the high cost of manual annotation, and inefficient processing of multi-source data. [Method/Process] We proposed the semantic understanding-enhanced, cue-engineered domain knowledge extraction technology system, constructed on the methodological framework of manually constructing ontology modelling. In order to process the acquired data, the ETL\MinerU and other tools were used, and the DeepSeek-R1application programming interface was then invoked for intelligent extraction. The ontology model was designed based on domain cognitive features and the multi-source heterogeneous data fusion method was used to achieve the unified characterization of the data structure. Furthermore, the DeepSeek and knowledge extraction were combined. Our system provides a cost-effective reusable technical paradigm for constructing domain knowledge graphs, as well as efficient knowledge extraction, leveraging the advanced powerful textual reasoning ability of the DeepSeek model. [Results/Conclusions] In this study, we take the construction of a domain knowledge map of the entire pig industrial chain as an empirical object. We define the structure of the industrial chain, identify 21 types of core entities and describe their attribute relationships. We achieve the knowledge modelling of the pig industry with a focus on smart farming. The methodology developed in this research was also employed to process and extract knowledge from online and offline resource data. Preliminary experiments demonstrate that DeepSeek-R1 exhibits an F1 value of 0.92 when recognizing the attributes of 161 diseases and 11 types of entities in pig disease control scenarios under zero-sample learning conditions. These experiments also ascertain the reusability of the methodology for other links in the chain. Concurrently, the constructed knowledge map of the entire industrial chain of pigs will be utilized for the design and validation of intelligent application scenarios, with the objective of promoting the intelligent information processing in the pig industry. This study proposes a synergistic paradigm for constructing domain knowledge graphs using DeepSeek, a method that combines deep learning with manual calibration for efficient knowledge extraction and ensure accuracy. This approach ensures the efficiency of knowledge extraction and verifies the knowledge extraction potential of LLMs in vertical domains. The study's findings contribute to the extant literature and offer a practical reference for the promotion of DeepSeek-enabled cost-effective construction of knowledge graphs.

[Purpose/Significance] Red cultural relics are a testimony to the arduous and glorious struggle of the Communist Party of China and its most precious spiritual wealth. In recent years, with the development of digital technology, the digital construction of red relics has made remarkable progress. However, the digital construction of red cultural resources is a complex and multi-dimensional process that still faces numerous challenges. With the comprehensive promotion of the Development Action Plan for the Trusted Data Space (2024-2028), the circulation of data elements, the co-creation of value, and security governance have become key issues in digital construction, which also brings new opportunities for the digital construction of red cultural resources. [Method/Process] Through literature review and online survey, we summarized the achievements made in the theoretical research and practical exploration of the digital construction of red cultural resources, and analyzed the challenges faced in terms of data circulation, technical application, security protection, governance mechanisms, talent and financial support. From the strategic, resource, technical, and social levels, we expounded on the value and significance of the construction of trusted data space in facilitating the digital construction of red cultural resources, and conducted a preliminary exploration of the construction approaches for the trusted data space. [Results/Conclusions] In terms of the key construction points, an operational framework for the trusted data space of red cultural relics will be established around three dimensions: construction of the data space supply system, construction of the core competence system, and cultivation of various types of data spaces. In terms of the implementation path, measures for the construction of the trusted data space of red cultural resources are proposed in four aspects: policy and system, technological empowerment, talent strategy, and social co-governance. Specifically, we provide institutional guidance in four aspects: improving the standardized management system and supervision mechanism, formulating technical specification standards, establishing a capital investment support mechanism and distribution system, and improving a dynamically optimized evaluation and feedback mechanism. We are providing technical empowerment in four aspects: conducting core technology research and development, strengthening the supply of basic capabilities, focusing on the development and application of artificial intelligence, and attaching importance to information security protection. We adopt the talent strategy of "attract, cultivate, utilize and retain" to build a high-level talent team for the trusted data space. We conduct social governance in three aspects: strengthening the overall planning and coordination of the government, promoting exchanges and cooperation among enterprises, and encouraging the public to jointly participate in building a new publicity and promotion matrix for the trusted data space of red cultural resources. The aim is to provide new perspectives for the quality development of the digital construction of red cultural resources.

[Purpose/Significance] The library is currently in a critical period of development for the "15th Five-Year Plan", and the intelligent strategy is one of the key areas of the library's "15th Five-Year Plan". The large-scale modeling technologies represented by DeepSeek, ZhipuAI, ChatGPT, etc. are reshaping the boundaries and forms of knowledge services through the deep integration of new-generation artificial intelligence technologies and knowledge service systems, providing important theoretical and technical support for the development of library intelligence strategies during the 15th Five-Year Plan period. Therefore, exploring how DeepSeek enhances library knowledge services has become one of the most cutting-edge issues worth paying attention to in the library and information science (LIS) field. [Method/Process] On the basis of a brief review of the current state of research on the integration of DeepSeek and library knowledge service theory, this article designs and proposes a theoretical model for DeepSeek to enhance library knowledge services. It explores the innovative model of DeepSeek that empowers library knowledge services from five aspects: knowledge discovery, knowledge acquisition, knowledge analysis, knowledge recombination, and knowledge utilization and thoroughly analyzes the four core dimensions of technology empowerment, business empowerment, user empowerment, and ecological empowerment. It also elaborates on the security issues of large models caused by the open source strategy, the intellectual property risks caused by technological innovation, the knowledge illusion problems caused by data traps and defects, and the information cocoon problems caused by technological applications. This study aims to provide some reference and inspiration for the research of related issues. [Results/Conclusions] The library is currently in a critical period of development for the "15th Five-Year Plan". DeepSeek's inherent technological advantages such as low cost, high performance, and open source ecosystem not only enable the library knowledge service system in multiple dimensions, reshape the boundaries and forms of knowledge services, comprehensively enhance users' knowledge service experience, but also provide stronger impetus for library construction, management, and service in the "15th Five-Year Plan" period. The theoretical model of DeepSeek empowerment of library knowledge services mainly includes four core dimensions: technology empowerment, business empowerment, user empowerment, and ecological empowerment. It has an impact on library service innovation in five aspects: knowledge discovery, knowledge acquisition, knowledge analysis, knowledge recombination, and knowledge utilization. At the same time, it can bring many problems, such as model security, intellectual property risks, knowledge illusions, and information cocoons. From the existing public information, DeepSeek can provide important technical support and core driving force for library knowledge service innovation in the era of artificial intelligence from four aspects: technical algorithms, training cost, open source ecology, and local lightweight deployment. Since the gradual formation of the DeepSeek open source ecosystem, more and more enterprises, communities, research institutions, teams, and developers have actively participated in and built the industry ecosystem, showing a strong magnetic field effect. Libraries should adhere to the principle of "join if you can't win", actively integrate into the DeepSeek open source ecosystem, and build an ecosystem of knowledge service ecosystems with library industry characteristics and disciplinary features.

[Purpose/Significance] The proliferation of generative artificial intelligence (AIGC) platforms has ushered in a transformative era for content creation. However, the industry is facing significant challenges due to technological homogenization. Platforms struggle to retain users, particularly Generation Z (those born between 1995 and 2009), due to standardized architectures, overlapping data sources, and repetitive training methodologies. Generation Z exhibits low loyalty and high migration tendencies. They are digital natives whose behaviors are shaped by unique socio-technological traits. They prioritize immersive experiences, thrive in circle culture, and rely heavily on peer-driven decision-making. However, existing studies primarily focus on generic user groups, and the ways in which these distinct characteristics influence sustained engagement with AIGC tools. This research bridges the gap by integrating the Stimulus-Organism-Response (SOR) framework with Generation Z's behavioral patterns, creating a new theoretical model that explains their constant usage intentions. These findings advance the theoretical discourse on user behavior in AI-driven ecosystems. They also offer actionable strategies for platforms to differentiate themselves in a user-centric way. This addresses critical challenges in an increasingly saturated market. [Method/Procedure] Guided by the Stimulus-Organism-Response (SOR) theoretical framework, this study proposed a model to examine the impact of xternal stimuli, such as circle influence, online word-of-mouth, and platform quality on continuous usage intention, as mediated by satisfaction and immersive experience., Individual innovativeness is considered to be a moderating factor. Data were collected via an online questionnaire distributed to 356 Gen Z users with experience using AIGC platforms. A 7-point Likert scale was used to measure constructs. Structural equation modeling (SEM) was employed to test the hypothesized relationships, including reliability and validity checks, as well as mediation effect, and moderation analyses. [Results/Conclusions] The findings reveal that circle influence, online word-of-mouth, and platform quality have a significant and positive impact on user satisfaction and immersive experience, These factors then mediate the relationship with continuous usage intention. Among these factors, circle influence demonstrates the strongest effect on satisfaction, highlighting Gen Z's social identity and dependence on their peers. Although platform quality is less dominant than social and reputational factors, it remains a foundational driver of user experience. It was found that individual innovativeness positively moderates the relationship between immersive experience and continuous usage intention. This indicates that users with higher innovativeness derive greater satisfaction from interactive experiences, which enhances their loyalty. However, no significant moderating effect of individual innovativeness was observed between satisfaction and continuous usage intention. Accordingly, the following suggestions are put forward to promote users' continuous usage intention. These suggestions include optimizing online word-of-mouth, strengthening circle operation, and enhancing the guidance of innovativeness. Future research could focus on exploring the differences in continuous usage intention among different types of AIGC platforms for Generation Z. Additionally, the model of influencing factors could be further refined to consider more complex real-world scenarios.

[Purpose/Significance] The evolution of smart libraries has ushered in a new era, marked by the integration of multimodal learning technologies that combine information from various modalities such as speech, images, and video. This cutting-edge technology is revolutionizing traditional information service systems by providing a more interactive, efficient, and personalized user experience. Unlike traditional studies that focus on single-mode interactions, this research examines the role of multimodal technologies in transforming library services and increasing user engagement. The study highlights its unique contributions to the field of library science, particularly in improving knowledge dissemination, enhancing user-centered services, and addressing emerging challenges in digital information management. These findings not only enrich the theoretical framework of smart libraries, but also provide practical insights into the design and deployment of advanced information services. [Method/Process] This study takes a multidisciplinary approach, drawing from library science, information technology, and human-computer interaction theories. It systematically reviews the historical development and theoretical foundations of multimodal learning technologies while emphasizing their relevance to intelligent library ecosystems. The analysis is organized around key application areas, including intelligent navigation, intelligent question and answer systems, user education with intelligent support, and immersive reading experiences. These areas were explored through a combination of case studies, and a detailed analysis of current library practices. To evaluate the practical impact of these technologies, the study employed qualitative methods, analyzing user feedback and system performance metrics. This comprehensive research also identifies current barriers to adoption, such as data privacy concerns, technology costs, and disparities in user acceptance across different demographics. [Results/Conclusions] The results show that multimodal learning technologies significantly enhance the functionality and user experience of smart libraries. They improve the accuracy of information retrieval, enable more interactive and immersive learning environments, and enable personalized services tailored to individual needs. Despite these advantages, challenges remain, particularly in areas such as securing user data, reducing deployment costs, and increasing accessibility for underprivileged users. The study proposes actionable strategies to address these issues, including enhancing system interoperability, refining ethical frameworks, and fostering human-computer collaboration to reduce barriers to technology adoption. It also identifies gaps in current research, such as the need for more empirical studies of long-term user interaction patterns and the scalability of multimodal systems in large library networks. Future studies could also explore the integration of emerging technologies such as augmented reality (AR) and artificial intelligence (AI) into multimodal library services to further improve their efficiency and reach. By providing a robust framework and practical strategies, this study contributes to the ongoing discourse on smart library innovation, and paves the way for more sustainable and inclusive information service models. It underscores the transformative potential of multimodal technologies to redefine library science and advance the global digital information landscape.

[Purpose/Significance] Unclassified sensitive information, a special type of information, that falls between state secrets and public information, may threaten national scientific and technological security if it is not managed properly. This study aims to clarify the conceptual connotation and management value of unclassified sensitive information. Additionally, it seeks to improve China's information security protection system by promoting the country's "national unclassified sensitive information management system". [Method/Process] First, based on literature research and content analysis, the characteristics and attributes of unclassified sensitive information and the necessity of its management are systematically explained. Second, the management experience of controlled unclassified information (CUI) in the United States is analyzed by means of case studies. These studies include the creation of special laws and regulations to unify the CUI management, the formation of specialized management institutions to create a complete organizational structure, the development of a CUI management system, and the establishment of CUI classification and labeling. Finally, it proposes strategies to promote the development of China's sensitive information management system, given the current state of management situation in China, with the aim of promoting the safe and efficient management of public information resources. [Results/Conclusions] China needs to strengthen the top-level design of its management system for unclassified sensitive information. First, China should establish a policy and standard system for managing unclassified sensitive information, and clarify the rules for defining and controlling it. Second, the organizational and institutional structure should be improved for the management of unclassified sensitive information, and strengthening the review of information disclosure. Third, efforts should be made to develop a management system for registering directories and labels of sensitive information, and to realize standardized and dynamic management of directories of sensitive information. Fourth, the dissemination, use, training and management check should be strengthened, and thus long-term and healthy management of unclassified sensitive information can be promoted. Fifth, a registration directory and labeling management system for sensitive information should be developed so as to achieve standardized and dynamic management of the sensitive information directory. Finally, efforts should be made to strengthen the dissemination and use of sensitive information, as well as training and management inspections, so as to promote the long-term and healthy management of unclassified sensitive information.

[Purpose/Significance] Amid the global wave of digital transformation in education, artificial intelligence (AI) has emerged as a driving force behind Japanese educational reform, propelling the country's education system toward an "AI+" model. The "Approved Program for Mathematics,Data science and AI Smart Higher Education" (MDASH), led by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), outlines a comprehensive framework for designing and implementing AI literacy (AIL) education in Japanese universities. MDASH not only reflects the Japanese strategic response to the AI-driven future, but also provides valuable theoretical references and practical guidance for enhancing AIL education in China. This study provides a detailed analysis of the "MDASH literacy-level" (MDASHL) curriculum model design, paying a particular attention to the model's modules and the mechanisms of interaction between them. It also examines the theoretical references from MDASHL review system to the AIL framework studies. The study proposes innovative implementation strategies for AIL education from unique perspectives, especially the "industry-academia integration" aspect. [Method/Process] Using internet research and literature analysis, starting with an exploration of Japanese national AI policy landscape, the study traces the evolution of Japanese AI policies and the contextual origins of the MDASH. It describes the objectives and philosophy of Japanese AIL education and delves into the theoretical underpinnings of the MDASHL curriculum model based on the mapping relationship between indicators of AIL frameworks and the components of the MDASHL review system. We select Hokuriku University, Wakayama University, Chiba University, and Kansai Univerisity as samples because they were approved by MDASHL and demonstrated exemplary effects. We introduce their subject curriculum design and specific teaching initiatives, identify the commonalities and unique characteristics of their AIL education, and further elaborate on their specific educational implementation pathways. [Results/Conclusions] The findings indicate that the Japanese MDASHL curriculum model is deeply rooted in the AIL frameworks. It summarizes five educational directions for Japanese AI literacy education: recognition, realization, comprehension, ethics, and practical operation. By comparing the current status of AIL education in China and Japan, the study found that Japanese AIL education has achieved rapid responsiveness and systematic development under the unified coordination of MEXT. It suggests that Japanese AI literacy education strategies have localized value, from which beneficial insights can be drawn in three areas: strategic planning, curriculum design, and industry-academia integration. These strategies provide innovative solutions for developing AIL education systems in Chinese universities. However, this study acknowledges limitations in the sample size. To comprehensively capture the full landscape of Japanese AIL education development, future research should expand the sample size, summarize its patterns and characteristics more thoroughly, and enhance the persuasiveness and generalizability of the findings.

[Purpose/Significance] Integrating culture and tourism is key to promoting rural revitalization. Constructing a scientific evaluation system and exploring differentiated development paths are core issues for achieving rural cultural and tourism development. [Method/Process] Taking 26 mountainous counties in Zhejiang Province as the research object, a research framework incorporating multi-dimensional analysis methods was constructed based on user-generated content data from tourism platforms. First, travel journal texts were collected to build a cultural and tourism integration database. Second, the BERTopic model was used to identify the potential thematic elements in tourists' narratives. Third, sentiment analysis was applied to quantify the emotional value of different themes in each county. Finally, fuzzy-set qualitative comparative analysis (fsQCA) was employed to reveal the complex development paths of cultural and tourism integration. [Results/Conclusions] 1) Through global topic modeling, eight themes of cultural and tourism integration were identified in 26 mountainous counties in Zhejiang Province, including ecological landscapes, traditional settlements, food culture, transportation, cultural activities, cultural heritage and arts, accommodation facilities, and leisure industries. These eight themes are summarized into four conceptual categories of cultural and tourism integration: natural experience dimension, cultural experience dimension, service support dimension, and leisure consumption dimension. 2) Tourists generally hold a positive attitude towards cultural and tourism experiences in different counties. The natural and cultural experience dimensions are highly regarded, but the service support dimension shows uneven levels, and the leisure consumption dimension displays significant differences, hindering the improvement of the quality of cultural and tourism integration in mountainous counties. Ecological landscapes and traditional settlements constitute the core layer of tourism experiences, while accommodation and leisure business formats, as potential directions, still require further development and activation. The comprehensive performance of cultural and tourism integration in various counties presents a notable spatial pattern of "two poles in the north and south, a developing middle region, and relatively weak coastal areas". 3) Through configurational path analysis, the development paths of cultural and tourism integration in the 26 mountainous counties can be summarized into six configuration paths, including the "ecological landscape + traditional settlements + food culture + transportation" model, the "cultural heritage and arts + leisure industries + food culture" model, the "traditional settlements + food culture" model, the "ecological landscape + cultural activities + transportation" model, the "ecological landscape + cultural heritage and arts + leisure industries + food culture" model, and the "cultural heritage and arts + leisure industries + food culture" model. These paths differ significantly in their combinations of core elements, reflecting the differentiated development strategies of different regions in terms of resource endowment, cultural characteristics, and market positioning. Five of the paths highlight food culture as a core condition, reflecting its foundational role in cultural and tourism integration. Cultural heritage and arts and leisure industries jointly form the core conditions in three successful paths, highlighting the importance of combining in-depth cultural experiences with leisure activities. Accommodation facilities often appear as missing or marginal conditions, indicating that short-term stays are the main form of rural cultural and tourism.

[Purpose/Significance] Against the backdrop of an accelerating population aging trend, the integration of big data and intelligent services in multimodal healthcare and eldercare has become pivotal for enhancing the quality of medical and eldercare services. However, existing knowledge service systems for big data in healthcare and eldercare face challenges such as difficulty of integrating multi-source heterogeneous data, the absence of cross-organizational sharing mechanisms, and passive service models. [Method/Process] First, a cross-domain aggregation method is proposed for multi-source heterogeneous medicare big data, including: 1) A method for constructing a clinical, key-feature-based medical case knowledge database. It extracts and categorizes critical features from electronic medical records using natural language processing (NLP). 2) A natural language processing-based cross-domain disease risk factor mining framework. It identifies risk factors from social media via topic-enhanced word embeddings and clustering techniques. 3) An adaptive pointer-constrained generation method for medical text-to-table tasks. It leverages the BART architecture to transform unstructured medical text into structured tables. Next, a knowledge discovery method based on multimodal medicare big data is developed, including: 1) A medical decision support approach integrating case-based reasoning (CBR) and explainable machine learning. It aims to enhance diagnostic interpretability through ensemble learning and case similarity analysis. 2) A large-scale medical model-driven knowledge system. It utilizes multimodal data pretraining and domain adaptation to support the entire diagnosis-treatment process. 3) A personalized recommendation method based on temporal warning signals, generating precise intervention plans via collaborative filtering and dynamic updates. Finally, a smart service model for full-cycle evolving needs is constructed, including: 1) A health information supply-demand consistency matching framework combining deep learning and clustering techniques; 2) A multi-level, cross-scenario health demand and behavior dynamic modeling approach. [Results/Conclusions] The proposed methodological framework significantly improves the efficiency with which medicare big data is integrated and the capabilities of its knowledge services. Key outcomes include: 1) Enabling disease risk prediction and personalized interventions through deep integration of cross-organizational, cross-scenario medicare data via multimodal aggregation and semantic alignment. 2) The CBR-ECC model and WiNGPT large medical models enhance the interpretability and full-process coverage of medical decision-making. These models improve the accuracy of diagnoses made by primary care physicians by over 30%. 3) The temporal warning-based recommendation method increases the dynamic update efficiency of health interventions by 40% and user satisfaction by 25%; 4) Dynamic health demand modeling reveals core pain points for chronic disease patients, providing a basis for precision service strategies. This research provides the theoretical and technical support for developing a proactive health service system that is both data-driven and human-machine collaborative. This system will, advance the implementation of the Healthy China strategy and innovation in aging population governance.

[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] In the era of cloud computing, the Library Services Platform (LSP) failed to become a unified solution for libraries it promised to be. Now, it faces new development bottlenecks in the era of smart libraries. Its relatively rigid architecture, isolated data models, and limited intelligence level make it difficult to meet modern users' urgent demands for access to new resource ecosystems and proactive services. This limitation stems from the fact that existing LSPs are rooted in a resource management design philosophy. They lack native support for intelligence, personalization, and ecosystem integration, which hinders their ability to serve as a core component in the construction of smart libraries. [Method/Process] The rapid development of large language model (LLM) technology is promoting libraries to transition from digital intelligent phases into a new era of intelligent services. As AI agents are increasingly emerge as a core strategy for LLM applications, this paper proposes a next-generation LSP architecture called A-LSP, which is agent-oriented. The core of A-LSP consists of a three-layer logical model. 1) Layer 1: Compatibility & Tools - MCP Marketplace, serving as the foundation of the platform, this layer bridges the agent ecosystem with the external world. It transforms existing heterogeneous library systems (including legacy LSPs) and external tools into invocable "capability units" for agents through standardized protocols. 2) Layer 2: Orchestration & Intelligence－Agent Middleware. Functioning as the platform's "operating system" and "brain," this layer handles agent lifecycle management, task planning and decomposition, state and memory maintenance, and most crucially, the coordination of multi-agent collaboration. 3) Layer 3: Application & Ecosystem - Agent Marketplace. This functional layer serves users and developers, where various reusable agents encapsulating specific business logic are published, discovered, combined, and invoked, creating a rich application ecosystem. This architecture enables the implementation of new platform strategies without replacing legacy systems, establishing a modern technological platform with endogenous intelligence, inclusive compatibility, and an open ecosystem. This agent-based library service platform can be seen as a significant upgrade to existing LSPs, it drives their transformation from resource management-centric to agent service-centric, establishing itself as the library service platform for the AI era. [Results/Conclusions] Moreover, this paper puts forward a "Five Centers" construction demand framework for future libraries, namely, the Smart Resource Center, Smart Service Center, Smart Learning Center, Smart Scholarly Communication Center, and Smart Cultural Heritage Center, to build a blueprint for the integration of library technology and business. For each center, it delineates a representative complex application scenario and analyzes the underlying multi-agent collaboration processes, thereby clearly demonstrating A-LSP's deep integration with each center's operational logic and illuminating its profound impact on future library service models.

[Purpose/Significance] With the rapid development of generative artificial intelligence (AI) and large language models (LLMs), the role of "prompt librarians" has emerged. This study constructs a theoretical framework for prompt librarians and explores the rationality, feasibility, and significance of the transition of librarians to this role from the perspective of new quality productive forces. Driven by the new quality productive forces represented by AI, transforming librarians into prompt librarians can not only optimize application scenarios and user experience, but also improve work efficiency and effectively promote the intelligent transformation of libraries. There is currently no research on this topic in the existing literature. This study, for the first time, proposes a theoretical framework for prompt librarians and the implementation path for the transition of librarians, filling the research gap in this area. [Method/Process] Through a review of relevant national and international literature, this study examines the impact of AI on the role and positioning of librarians within the library industry. Taking the new quality productive forces as the theoretical foundation and driving factor, the study explains the necessity of the transition of public librarians to prompt librarians, and analyzes the rationality, feasibility, and significance of this transition. Furthermore, a theoretical framework for prompt librarians is constructed, encompassing concepts, scope of functions, work processes, and core competencies. Additionally, through the method of literature review and online surveys, the study examines the current status of information and knowledge services in public libraries, focusing on the top thirty libraries ranked by online influence in China. It identifies the major challenges faced by librarians in the transition. Based on the theoretical framework of prompt librarians and real-world challenges, the study explores the implementation path for the transition of librarians to prompt librarians, ensuring the scientific, logical, and innovative nature of the research. [Results/Conclusions] As an emerging role that combines the library industry with AI technology, prompt librarians, driven by user needs, explore the unique resources of their collections in depth, revitalizing literature, diverse information resources, and other materials through AI pathways. They act as guides and translators between knowledge and AI, effectively driving the intelligent transformation of libraries. However, the transition of librarians faces many challenges. To facilitate a smooth transition, this study proposes implementation pathways, such as the establishment of dedicated prompt librarian positions, a "three-step leap" training model for librarians, robust top-level planning, the construction of multi-modal resource service platforms, AI ethics considerations, and interdisciplinary collaboration. Through these explorations, the study aims to provide innovative ideas and practical guidance for the transition of librarians in the AI era, enrich research on the application scenarios of new quality productive forces, and enhance the service quality and competitiveness of libraries.

[Purpose/Significance] Since the 19th National Congress of the Communist Party of China proposed the Rural Revitalization Strategy, China has placed high priority on the digital development of rural cultural resources, considerin it a key factor in the comprehensive revitalization of rual areas. However, China's current efforts to digitize rural cultural resources still face structural challenges, including insufficient funding, a lack of unified technical standards, a shortage of professional talent, and weak endogenous motivation. Against this backdrop, studying successful international experiences, particularly those from Japan, which has a rural social structure similar to China's, can provide valuable insights into exploring sustainable digital pathways in China. [Method/Process] Grounded in Embeddedness Theory, this research develops a four-dimensional analytical framework tailored to the characteristics of Japan's rural cultural resource digitization: institutional embedding, technological embedding, cognitive embedding, and autonomous practice. The study examines how rural communities overcome digitalization challenges by integrating external resource embedding with endogenous motivation activation through systematic collection and comparative analysis of 20 representative case studies from Japanese villages. [Results /Conclusions] The study reveals that, although Japanese villages commonly encounter practical constraints such as budget shortages, limited technical support, and a lack of professional expertise in digitizing cultural resources, some have successfully transformed through innovative approaches. Key lessons include: 1) institutional embedding: social organizations establish precise and sustainable funding networks through specialized grants to compensate for insufficient government investment; 2) technological embedding: specialized enterprises provide customized solutions and integrated platform services to address "data silo" issues; 3) cognitive embedding: universities and non-governmental organizations (NGOs) enhance villagers' digital literacy through knowledge transfer and talent cultivation, fostering cultural identity; and 4) autonomous practice: villagers, driven by crisis awareness and cultural consciousness, initiate self-organized digitization efforts. They transition from "external" processes, such as blood transfusion to "endogenous" processes, such as blood generation. Implications for China focus on the following: 1) refining an institutional embedding mechanism guided by government policies and fueled by social participation, including dedicated funds and tax incentives; 2) promoting technology transfer from "niche-leading " enterprises to develop modular tools and open resource platforms; 3) strengthening collaboration between the government, industry, universities, research institutions, and application developers to nurture local digital talent through academic support and NGO mobilization; and 4) empowering villagers as active participants, applying digital outcomes in education, tourism, and other scenarios to create synergy between cultural preservation and industrial development. Although this study has established a representative sample set through rigorous case selection criteria, several limitations should be acknowledged. First, due to the lack of transparency in Japanese government, some cases with incomplete implementation details were excluded from the in-depth analysis. Future research should include on-site investigations to collect primary data and address this gap. Second, the current study relies primarily on literature and publicly available data. The next phase involves field research in Japan. Mixed methods such as in-depth interviews, participatory observation, and questionnaire surveys, will be employed to verify the accuracy of case data and explore the mechanisms of stakeholder interaction in the digitalization process. This approach will increase the breadth and depth of the research.

[Purpose/Significance] The advent of the digital era has presented China with significant opportunities to modernize rural governance. Digital literacy is crucial for enabling farmers to participate in rural governance and promote the development of digital villages. Rural residents are direct participants in digital village development, and their digital competence fundamentally determines the modernization level of rural governance. Their proficiency in digital skills affects not only the effectiveness of intelligent rural management, but also serves as a key indicator for measuring the progress of digital rural development. [Method/Process] Based on a literature review and sociopolitical reports, this study combines research objectives and thematic focus to create a questionnaire that investigates farmers' digital competence and rural governance performance. A stratified sampling strategy was implemented to select 306 rural households from diversified villages across township and sub-district jurisdictions, in Xia County, Shanxi Province, for the case study. The dataset encompasses respondents' demographic attributes, familial characteristics, digital proficiency metrics, and multidimensional indicators of rural governance efficiency and capacity building. Through integrated application of exploratory factor analysis (EFA) and multivariate linear regression modeling, this investigation systematically examines the determinants through which digital literacy influences governance outcomes. Together, these approaches establish a theoretical framework and evidence-based pathways for enhancing rural digital transformation initiatives. [Results/Conclusions] The empirical analysis results indicate that farmers' digital literacy significantly impacts rural governance efficacy. Specifically, improvements in the four dimensions of digital literacy - digital awareness, digital skills, digital application, and digital security - positively influence the efficacy of rural governance.In other words, the higher the digital literacy level of farmers, the greater the enhancement in rural governance efficacy. Among these, digital security literacy has the most significant effect on improving governance efficacy. Next is digital application literacy, followed by digital awareness literacy. Digital skill literacy exhibits a relatively weaker impact. Given the significant positive influence of digital literacy on rural governance efficacy, this paper proposes recommendations from three perspectives: strengthening farmers' proactive awareness of digital literacy, enhancing their knowledge in digital literacy, and improving the digital infrastructure construction in rural areas. These suggestions provide practical references for the digital development of rural governance in Xia County, Shanxi Province. Due to various constraints, however, the study only examined Xia County in Shanxi Province as the research area, resulting in notable geographical limitations in the sample. This is because rural regions in different areas exhibit significant disparities in economic development levels, cultural traditions, and policy support, all of which may affect farmers' digital literacy and the efficacy of rural governance. Consequently, the conclusions of this study may not accurately reflect the actual conditions in rural areas across diverse regions. To improve the generalizability of the findings, future research should expand the sample selection to include more representative areas.

[Purpose/Significance] As digital government accelerates, the artificial intelligence (AI) literacy of grassroots civil servants has become critical to promoting smart government management. Grassroots-level civil servants who possess high levels of digital and AI literacy are indispensable drivers in establishing a digital and smart government. However, significant differences among grassroots civil servants in AI literacy and digital skills adaptation make it difficult for them to fully adapt to the requirements of smart government management. To effectively apply AI technologies in grassroots governance, it is essential for the effective application of AI technologies in grassroots governance to systematically identify its influencing factors and propose targeted cultivation paths, thereby improving public service quality and governance efficiency. [Method/Process] This study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to construct a TAM-IDT analytical framework. Based on empirical research identifying the AI literacy deficiencies of current grassroots civil servants, the TAM-IDT analytical framework systematically examines the impact mechanisms of key variables, perceived usefulness, perceived ease of use, and behavioral attitude, on AI literacy. The framework also proposes stage-based and group-specific cultivation strategies. The study uses local government civil servants as its research sample. It collects data through questionnaires and interviews, and employs structural equation modeling and mediation effect analysis for empirical validation. [Results/Conclusions] The findings reveal that behavioral attitude has a significant positive impact on AI literacy. Perceived usefulness notably enhances behavioral intention, while perceived ease of use has a negative effect on behavioral attitude, suggesting that individuals who perceive greater difficulty may be more motivated to learn. However, one of the highlights of this study is that civil servants who are proficient in AI technology or have used it in their work have a lower desire to learn more about it. Further analysis shows that perceived ease of use positively influences behavioral attitude indirectly through perceived usefulness. Additionally, both cognitive variables indirectly affect AI literacy via behavioral attitude, forming a "cognition-intention-behavior" influence chain. Based on these results and the classification of stages and types of technology adoption using Innovation Diffusion Theory (IDT), a three-dimensional, differentiated AI literacy cultivation strategy called "perception diffusion collaboration" was proposed. This strategy is based on the five elements, stages, and the groups of people involved in innovation diffusion. It offers a theoretical foundation and practical path for improving AI literacy among grassroots civil servants and advancing the modernization of grassroots governance.

[Purpose/Significance] With the growing demand for intelligent cultural services, libraries are seeking innovative approaches to enhance access to and engagement with historical literature. Generative AI presents promising opportunities for transforming digital reading services, particularly in processing, interpreting, and promoting complex historical documents. This study investigates the integration of generative AI into library-based historical literature promotion, aiming to address persistent access limitations, foster more interactive user experiences, and optimize the depth and breadth of reading engagement. [Method/Process] This research adopts a multi-method approach combining literature review, comparative platform observation, and empirical implementation practice. The study focuses primarily on Shanghai Library's historical digital collections and AI-enabled services. It develops a structured three-layered implementation framework encompassing the data layer, application layer, and service layer-each mapped to corresponding technical and operational phases of digital reading promotion. Within this architecture, a six-step service pathway is articulated: demand analysis, activity planning, content mining, multimodal interaction, content review, and intelligent recommendation. Extensive practical experimentation is conducted across these stages. Key innovations include the application of Retrieval-Augmented Generation (RAG) to support complex historical document Q&A; the use of multimodal creative tools (e.g., Midjourney) to generate engaging visual materials; implementation of voice-based AI interactions to improve accessibility for diverse user groups; and the deployment of dynamic content management modules for librarians to curate and monitor AI-generated materials. Additionally, backend tools such as user profiling dashboards, personalized push notification systems, and topic-based knowledge repositories are developed and tested to enhance librarians' ability to deliver targeted and data-driven reading promotions. [Results/Conclusions] The findings demonstrate that generative AI significantly enhances the efficiency, precision, and user engagement levels of historical literature services. AI-driven methods substantially improve OCR accuracy, streamline metadata generation, facilitate both visual and semantic content creation, and enable real-time interactive services via natural language interfaces. These advancements contribute to a more immersive and responsive digital reading experience. However, several challenges persist, including limited availability of domain-specific training data, the ongoing risk of AI-generated content inaccuracies (hallucinations), and unresolved intellectual property considerations. The study emphasizes the importance of developing domain-specific large language models, establishing expert-assisted validation mechanisms, and formulating clear legal and ethical guidelines for AI-generated content in the library context. While the prototype platform developed in this research exhibits notable gains in user engagement and librarian workflow support, its long-term sustainability hinges on fostering cross-institutional resource collaboration, advancing supportive policy frameworks, and embedding robust ethical safeguards. Future research directions include the exploration of adaptive AI training systems incorporating user feedback loops, integration of cross-library data resources, and the enhancement of multilingual AI capabilities to better serve diverse and global user communities.

[Purpose/Significance] This study focuses on the participation behavior of users prone to depression who participate in user-generated content (UGC) platforms, aiming to explore their behavioral heterogeneity and the underlying influencing mechanisms. The research aims to expand the theoretical scope of studies on user behavior while providing UGC platforms with practical guidance on building differentiated user care models and refining operational strategies. By utilizing authentic user-generated content as the data foundation, this study addresses the representational limitations commonly associated with traditional small-sample approaches, such as surveys and interviews. It introduces a data-driven perspective and methodological innovation to the field of information behavior research. Furthermore, this study enhances the understanding of varying psychological and behavioral needs among different types of depression-prone users. The findings can assist platforms in optimizing user experience, improving emotional support systems within online communities, and informing the development of more targeted and responsive intervention strategies. [Method/Process] First, web scraping techniques were used to collect a large volume of depression-related posts from the Xiaohongshu platform as the primary data source. Second, representative keywords were extracted through Word2Vec and K-means clustering algorithms. A keyword co-occurrence network was then constructed using the Leiden clustering algorithm to identify semantic relationships. By integrating user attribute information, the study achieved a fine-grained classification of heterogeneous depression-prone user groups. Third, drawing on self-determination theory (SDT) and the technology acceptance model (TAM), and leveraging BERTopic for advanced topic modeling, the study constructed a comprehensive factor model to examine the mechanisms influencing user participation behavior in depth. [Results/Conclusions] The research identifies three distinct types of depression-prone users: adolescent depression expression, help-seeking expression, and emotional breakdown expression. Results indicate that posting and commenting behaviors across these groups are primarily driven by emotional needs and environmental factors. Emotional needs are the dominant motivator for active participation, while environmental influences significantly contribute to triggering interaction, especially within comment sections. Additionally, adolescent depression expression and emotional breakdown expression show stronger tendencies toward self-related needs, reflecting deeper emotional and identity concerns. In contrast, help-seeking expression exhibit more evident competence-related needs, focusing on practical advice and problem-solving. Although competence and technical factors account for a smaller proportion, they still play a meaningful supporting role in shaping the structure and substance of user participation behavior on UGC platforms.

[Purpose/Significance] Public emergencies frequently trigger online public opinion, exacerbating public panic and threatening social stability. The intrinsic linkage between public emergencies and online discourse amplifies the dissemination of public emotions, attitudes, and perspectives across online platforms, creating a feedback loop that influences event dynamics. Investigating the generation mechanism of public opinion on hot topics in such contexts provides critical theoretical foundations for mitigating cyber discourse risks, while enhancing the accuracy and efficiency of governmental mangement over online public opinion. [Method/Process] From an information ecology perspective, this study employs fuzzy-set qualitative comparative analysis to examine the online public opinion heat of 50 public emergencies between 2020 and 2022. We analyze eight conditional variables across four dimensions - information, information person, information technology, and information environment - including peak propagation speed, peak event popularity, netizen attention, opinion leaders' communication power, important media participation, central media coverage, the proportion of the overall public opinion field, and event duration. Single-factor necessity detection and configuration analysis were performed, and robustness was tested by adjusting calibration points and consistency thresholds. Finally, based on empirical findings, we interpreted case studies and proposed a mechanism for the generation of online public opinion heat in public emergencies. [Results/Conclusions] The results reveal that information and information people are the primary drivers and key causes of hot public opinion. Although information environment and information technology are not necessary conditions, they still contribute to the process. In public emergencies, multiple factors jointly influence online public opinion, and no single factor alone determines its intensity. Rather, the complementarity of multiple factors can, to some extent, substitute for seemingly necessary conditions. The key findings reveal that the event's peak plays a dominant role in driving high online public opinion intensity, and directly triggers its rapid outbreak, while the absence of major media participation and short event duration - core conditions for non-hot events - significantly reduce public engagement due to limited coverage and transient attention. Additionally, opinion leaders' communication power exhibits a strong positive correlation with public opinion on hot topics, as their amplified expressions attract more attention from netizens and further amplify the momentum of the discourse. These findings will provide valuable insights for effectively managing and controlling online public opinion during emergencies. Future research should examine the impact of emotional shifts, such as positive, negative, and neutral emotions, on the virality of online public opinion during emergencies, while also exploring the underlying mechanisms of such emotional shifts. Additionally, future studies should differentiate between policy stages in emergency development and examine how policy interventions shape the dynamics of public opinion. Finally, network analysis techniques (e.g., forwarding relationship networks, key evolutionary network structures) should be employed to uncover the mechanisms that drive public opinion heat in emergency-related discourse.

[Purpose/Significance] This paper investigates how generative artificial intelligence (GenAI) is reshaping the Searching as Learning (SAL) paradigm, focusing on its implications, challenges, and prospects in Library and Information Science (LIS). Traditional SAL emphasizes the cognitive and metacognitive processes by which users acquire and construct knowledge through information retrieval. However, the advent of GenAI - especially large language models (LLMs) - introduces a transformative shift from keyword-based querying to dynamic, dialogic, and multimodal interactions. This study aims to clarify the conceptual and practical significance of GenAI-driven SAL, explore its technical trajectories, and evaluate its impact on learners' behavior, learning strategies, and information literacy. It also highlights the emerging ethical and epistemological challenges posed by GenAI systems in learning-oriented search contexts. [Method/Process] Using the PRISMA-ScR framework, this study conducted a scoping review of academic and gray literature published between January 2023 and May 2025. A total of 1 681 records were retrieved from major academic databases and preprint repositories. After screening titles, abstracts, and full texts, 22 studies were selected for in-depth qualitative analysis. Thematic coding and synthesis were conducted to extract recurring patterns and theoretical insights across three key dimensions: GenAI-enhanced search technologies, evolving user behaviors in SAL contexts, and normative concerns associated with credibility, agency, and transparency. The analysis was grounded in LIS theories, including information behavior, metacognitive models of learning, and digital/information literacy frameworks. [Results/Conclusions] The results reveal that GenAI is fundamentally reshaping SAL in three key areas. First, in terms of technology, GenAI systems (e.g., GPT-based chat interfaces) provide conversational, context-aware, and multimodal assistance, transforming SAL from reactive searching to proactive co-learning. These systems scaffold learning through adaptive query reformulation, real-time content summarization, and source triangulations supporting iterative reflection and cognitive engagement. Such affordances mirror the functions traditionally associated with human tutors, thereby expanding learners' capacity for critical inquiry and self-directed exploration. Second, user behaviors in SAL are undergoing a paradigm shift. Learners increasingly engage in human-AI co-construction of knowledge, participating in iterative query-dialogue loops that facilitate concept clarification and knowledge synthesis. While this enhances engagement, personalization, and perceived learning efficiency, it also raises concerns. Over-reliance on AI-generated content may undermine learners' critical thinking, reduce information discernment, and promote passive consumption. The study identifies a dual effect. While GenAI augments higher-order thinking and strategic learning, it can also lead to superficial comprehension when learners lack the skills to critically evaluate AI output. Third, the review underscores the urgency of addressing ethical and pedagogical challenges. Issues such as AI hallucination, algorithmic opacity, and biased content threaten the credibility of GenAI-enhanced learning environments. From an LIS perspective, this necessitates a reconfiguration of information literacy education to include AI literacy. Students must be equipped not only to retrieve and evaluate information, but also to interrogate algorithmic sources, verify provenance, and triangulate AI outputs with authoritative references. GenAI should be positioned as a cognitive assistant, not a definitive knowledge authority. GenAI holds substantial promise in enhancing SAL through greater interactivity, personalization, and cognitive scaffolding. However, these benefits must be balanced with informed practices that mitigate risks to learner autonomy, critical reasoning, and information ethics. This work establishes an analytical foundation for future research and practices at the intersection of AI, learning, and information behavior.

[Purpose/Significance] Generative artificial intelligence (GAI) is currently advancing at an astonishing pace. GAI has unleashed remarkable potential in various fields and is significantly fueling social and economic development. However, this rapid progress has also given rise to a plethora of complex issues, including but not limited to data security breaches, privacy violations, the spread of false information, and intellectual property infringements. Existing research primarily focuses on the governance of AI in general, leaving a gap in in-depth exploration of GAI. This study aims to fill this void by meticulously comparing the governance approaches of Europe and the United States in the realm of GAI. Through this comparison, the study aims to provide valuable insights for China to refine its own governance system. This is not only crucial for China's domestic technological development and social stability but also plays a pivotal role in promoting the harmonization of the global governance framework for GAI. [Method/Process] This research adopts a multi-faceted approach. It commences with a comprehensive review of relevant literature, gathering insights from a wide range of academic sources to understand the current state-of-the-art in GAI governance in Europe and the United States. Additionally, it deploys the case-study method, examining real-world examples such as the development of OpenAI's GPT series in the US and the implementation of the EU's AI Act. By analyzing these cases, it can vividly illustrate the practical implications and impacts of different governance strategies, thus enabling a more in-depth and accurate comparison. [Results/Conclusions] We found that the European Union adopts a regulatory path centered on data protection and ensures the fairness and sustainability of technological development through a strict legal framework. However, this strong regulatory model may stifle innovation vitality to some extent. The United States adopts a governance model oriented towards market accountability, emphasizing technological innovation leadership and free development. It stimulates market vitality through industry self-discipline and flexible regulation, but there is a hidden danger of insufficient ethical risk control. Based on these findings, this paper recommends that China adopt a balanced approach. China should integrate elements of both the U.S. and E.U. models to foster innovation while ensuring ethical and legal compliance. Future research could explore ways to adapt these governance models to emerging trends such as integrating GAI with other emerging technologies and addressing the unique governance challenges posed by cross-border data flows.

[Purpose/Significance] In the digital age, digital literacy is a core competency that individuals need to cope with challenges and achieve comprehensive development. A digital literacy assessment is the starting point for the improvement of citizens' digital literacy. It enables individuals to understand their level of digital literacy and provides organizations with the information they need to develop digital literacy construction policies and conduct education and training programs. At present, no literature is available on the comparative study of open digital literacy assessment tools. [Method/Process] First, we used keywords to retrieve information related to digital literacy assessment tools, and then we browsed and filtered the search results. Next, we read the information on the official website of the assessment tools and tried the tools, and finally we selected the tools. The article introduces eight online, open digital literacy assessment tools: MyDigiSkills tool, Digital Skills Accelerator tool, Ikanos test for citizens, Test your digital skills tool, The Digital Competence Wheel tool, Pix test, Digital Citizenship test, and Northstar Digital Literacy Assessment test. We carried out comparative research on these assessment tools from the following perspectives: development institution, assessment object, assessment framework, test design and assessment result. [Results/Conclusions] The article makes some suggestions for China regarding the formulation of national digital literacy frameworks, the development of online open digital literacy assessment tools, and the implementation of digital literacy education. The Party and State institutions should formulate the frameworks, along with the universities, industry associations and other institutions, as well as researchers in relevant fields. The framework should include the following aspects: the capability system, capability level, and capability description or examples. Organizations can actively develop digital literacy assessment tools for different groups based on the frameworks, adapting them as needed. One type of question can be used by itself or in combination with other types. The assessment tools should display the user's overall digital literacy level and the results of each ability assessment. This allows the user to understand their own digital literacy level and identify areas for improvement. The assessment results can be differentiated, and users can select their level or score form. Training and educational institutions should consider offering training courses related to various abilities. The courses should vary in difficulty to meet the needs of different groups and individuals.

[Purpose/Significance] With the rapid development of artificial intelligence generated content (AIGC) technology and the deepening of social impact, it is an important responsibility and historical mission of university libraries to cultivate and enhance students' critical information literacy (IL) in the application of AIGC. The research aims to explore the content and pedagogical strategies of critical IL education in university libraries for AIGC applications, promote the ability of college students to critically recognize and apply AIGC in the AI era, and also provide reference for the development of critical IL education in university libraries. [Method/Process] By reviewing the relevant literature at home and abroad, this paper summarizes the research status of critical IL education for AIGC applications. Based on the requirements of the "Higher Education Information Literacy Framework" for the cultivation of critical thinking ability, the current situation of critical IL education in university libraries, and the relevant policies and guidelines for the development of AI literacy education at home and abroad are reviewed. The content of critical IL education in university libraries for AIGC applications can be categorized into three aspects: AIGC application knowledge, AIGC application skills, and AIGC application ethics. At the same time, based on the requirements of IFLA library's Strategic Response to Artificial Intelligence' and the lack of IL education system in university libraries, it is proposed that the critical IL education of university libraries from the perspective of AIGC application should be ensured and implemented from the aspects of educational content integration, educational team building, educational mode development and educational system optimization. [Results/Conclusions] The research on critical IL education for AIGC application has a critical role in promoting the cultivation and improvement of students' critical thinking ability for AIGC. University libraries should be aware of their responsibilities, actively respond to the new requirements of critical IL education for AIGC applications, innovate and expand the content and form of IL education, and help students acquire the new IL skills needed for AIGC applications. At the same time, university libraries should also continuously update the content of critical IL education from the perspective of AIGC application, and have the courage to explore new teaching methods and strategies, so as to better cultivate and improve students' IL of AIGC application, help students use AIGC scientifically, correctly and normatively, and realize lifelong learning.

[Purpose/Significance] With the rapid advancement of the information society and the ongoing construction of smart cities, public libraries are facing increasing pressure to transition into a smart service model. Smart services leverage cutting-edge technologies to enhance user experiences and improve the efficiency of library services. Public libraries in China, however, are encountering challenges such as mismatched service offerings, unsatisfactory user experiences, and inadequate technological implementation as they move toward a smart service model. It is crucial to identify how to optimize this transition in a manner that prioritizes user needs, ensuring that smart library services meet the demands of a diverse user base. This research aims to explore the dynamic relationships among users, technology, content, and the service environment in public library smart services, thereby promoting innovation and addressing diverse user requirements. [Method/Process] The study develops a model to analyze the influence of various factors on the user experience of smart services in public libraries. Adopting Actor-Network Theory (ANT) constructs an integrated framework for understanding the interactions between various actors in the smart service ecosystem. By combining both online and offline surveys, the research captures library users' perceptions of their smart service experiences and identifies the critical factors that influence user experience and provides valuable data support and strategic recommendations for optimizing smart library services.Principal component analysis is used to identify the key factors affecting user experiences. [Results/Conclusions] The findings show that the core factors influencing user experience in smart services include: "advanced technology support," "network compatibility and flexibility," and "convenient communication channels" within the "library technology actors" dimension; "usability, operability, clarity, and comfort of portal browsing" within the "interaction between human-technology actors" dimension; "effort expectancy, information literacy, and time-energy consumption" within the "user (human actor)" dimension; "professionalism and competence of library staff" within the "librarian (human actor)" dimension; and "social influence and facilitating conditions" within the "interaction between human actors" dimension. These factors have a positive impact on the user experience, with particular attention required for the factors related to the "technology actors" dimension. Libraries need to focus on improving three factors in this area while maintaining and further optimizing the other factors. "Human-technology interaction" activities are crucial in improving the usability and user-friendliness of smart services, especially in more complex technological settings. Social influence and enabling conditions play an important role in enhancing user trust and their overall experience. Based on the empirical findings, the study proposes optimization strategies for public library smart services from three dimensions: the technical actors of the smart service system, the "human-technology actors" interactions, and the "translation" activities among human actors. These strategies include enhancing multi-dimensional collaboration among technical actors in the smart service system, improving the sensory experience of users with the smart service terminals in libraries by increasing their ease of use, empowering digital literacy, and optimizing innovation spaces to drive bidirectional reader participation. The aim is to provide a specific guide for the design and optimization of smart library services.

[Purpose/Significance] The confluence of digital transformation and the Fourth Industrial Revolution has driven the emergence of data science as an interdisciplinary field. Data science leverages structured and unstructured data to discover knowledge and support decision-making, thereby reshaping research paradigms in information science, computer science, and the social sciences. This study focuses on the development of data science degree programs within member institutions of the iSchools Consortium, a global alliance of information schools. Through systematic empirical investigation, the study aims to unveil innovative features in the program's disciplinary positioning, curriculum architecture, and talent cultivation models. This research aims to inform global information science education institutions on how to optimize their disciplinary strategies and curricular designs for data science. Ultimately, this will address the challenges of knowledge system reconstruction and talent development iteration within the traditional library and information science (LIS) discipline amid its digital transformation. [Method/Process] This study employed a web-based survey and content analysis methodology to create a multi-dimensional analytical framework based on the 2023 iSchools Consortium membership directory. Using a stratified sampling approach that integrated disciplinary influence, as measured by the QS World University Rankings, and program maturity indicators, including curriculum comprehensiveness and industry partnership networks, eight representative U.S. higher education institutions were selected as core samples. A systematic empirical investigation was conducted to thoroughly analyze the current landscape of data science degree programs. The study focused on four critical dimensions: 1) degree-awarding structures such as degree types, concentration specializations, and accreditation standards; 2) credentialing ecosystems such as micro-credentials, stackable certificates, and non-degree pathways; 3) curricular architectures such as core course clusters, elective modules, and interdisciplinary integration mechanisms; and 4) career trajectory outcomes, such as sectoral distribution, occupational roles, and industry-specific skill premiums. [Results/Conclusions] The study summarizes the current state of data science discipline education in international iSchools from several perspectives, including the characteristics of degree program offerings, the reconstruction of disciplinary positioning, pathways for curriculum integration, and insights into employment trends. Based on this, it makes recommendations for developing China's domestic data science discipline. These recommendations include optimizing the disciplinary layout, innovating the curriculum system, and deepening industry-education integration. However, it should be noted that this research is constrained by its small sample size of eight institutions and its geographical scope, which is limited to the United States. In the future, the study could expand to encompass members of the European iSchools consortium, such as the iSchool at University College London and the iSchool at Humboldt University in Berlin, as well as emerging data science programs in the Asia-Pacific region. Through cross-national comparative analysis, it aims to reveal how culture, policies, and industrial ecosystems impact disciplinary development differently. Furthermore, the study could incorporate Learning Analytics technology to model learner behavior in data science courses offered on MOOC platforms, such as Coursera and edX. This would facilitate the refinement of course module granularity and adaptability to better meet learners' needs.

[Purpose/Significance] Information literacy (IL) training for farmers has become one of the main contents for farmers in the new era. However, the current implementation of rural revitalization still does not pay enough attention to farmers. At the same time, farmers' IL ability is an important embodiment of farmers' integration into the digital countryside, which can give a strong boost to the modernization of agriculture and rural areas. Therefore, it is of great practical significance for the rural revitalization strategy in the new era to make full use of multiple social subjects and improve farmers' IL. [Method/Process] This paper reviews the concept and definition of IL, and analyzes the research on farmers' IL in recent years. The results show that most of the current research on the cultivation of farmers' IL focuses on a specific topic and lacks holistic research. Therefore, it is necessary to systematically understand the cultivation process of farmers' IL, and guide the cultivation behavior of IL by the all-round cultivation concept. [Results/Conclusions] At present, although the local governments have initially built an IL training model of the new era, with schools and social organizations as participants in the model, farmers still lack information knowledge, information awareness, and IL skills. Several proposals are put forward here to address the above issue. First of all,it is necessary to strengthen the construction of IL education system and improve farmers' information knowledge. The government should give full play to the local government agencies in resource integration, schools and scientific research institutions in professional advantages, and social organizations in providing information services, so as to provide farmers with more systematic IL training. Second, efforts should be made to jointly build IL education space to raise farmers' information awareness: the government should build farmers' IL training base, the schools should promote the transformation of the education model, and social organizations continue to carry out IL training project. The three parties join hands to build a three-dimensional integrated IL education space of "material space, spiritual space and social space", and a new way of the cultivating farmers' information awareness. Finally, IL teachers should be trained to improve farmers' information literacy. The government will attract and retain information talent in rural areas through positive talent polices. Schools will play an educational role in developing farmers' information literacy skills.

[Purpose/Significance] Public access policy plays a crucial role in raising the awareness of openness, promoting scientific progress and innovation development. Studying the current situation of scientific data sharing in international countries can provide a reference for the practice and development of scientific data sharing activities in China. [Method/Process] Over the past 15 years, an increasing number of funding agencies in the United States have responded to national policy calls to require funded projects to share the research results in order to improve the effectiveness of the grant implementation and to promote scientific development. To this end, many academic institutions have established and provided a variety of data support facilities and services, but these facilities and services are often scattered across different administrative departments. Data management and sharing activities under this model suffer from organizational deficiencies, fragmented activities, overlapping services, inaccessibility, and others that reduce the efficiency of public access to scientific data. In order to understand the reality of scientific data sharing, ARL conducted a fact-based study named the RADS initiative on the scientific data sharing model, with survey respondents coming from six research-intensive universities in the United States, who are involved in scientific data management and sharing, resulting in a relatively comprehensive survey. The article adopts the network research method and literature analysis method, through the interpretation of the first phase of ARL's RADS Initiative series of reports and materials, to comprehensively understand the composition of the entire life cycle of scientific data management and sharing activities, service content and implementation costs of the U.S. academic institutions under the public access policy. We also analyze the behavioral characteristics of the two main actors of the U.S. colleges and universities involved in the practice of scientific data sharing, the characteristics of the activities and support services, and summarize the real problems of scientific data management. The practical problems of scientific data sharing include inter-departmental coordination and linkage, gaps in supply and demand between disciplines, boundaries between disciplines, inadequate cost-benefit evaluation, and the availability of shared data to the public. [Results/Conclusions] On the basis of summarizing the successful experiences and shortcomings of the RADS Initiative, and taking into account the current situation of scientific data sharing in China, this paper puts forward the construction ideas and quality enhancement suggestions to promote the implementation of scientific data sharing activities in China at each level with an emphasis on public participation, We propose to integrate the coordinated development and optimize cost-effectiveness, foster the data literacy and emphasize user feedback, focus on the public access, and construct the core clusters.

[Purpose/Significance] High-quality development of new quality productive forces cannot be achieved without the support of intellectual property rights. Intellectual property (IP) has emerged as a new type of production factor with catalytic and leveraging effects, presenting both opportunities and challenges for the intelligent transformation and development of IP information literacy education in university libraries. This study aims to explore the current status and innovative paths of IP information literacy education in university libraries against the backdrop of new quality productive forces, providing theoretical references and practical foundations for the transformation and development of libraries. [Method/Process] Through a review of relevant domestic and international literature, it was found that existing research has primarily focused on investigating and analyzing the status of intellectual property information literacy education in universities, without incorporating an analysis of the demand for new quality intellectual property talent in the context of new quality productive forces. The article summarizes existing research and conducts an online survey of IP education practices in 30 national intellectual property demonstration universities, examining dimensions such as platform setup, participating entities, educational content, educational formats, branding, and special topic settings. From the perspective of participating entities and training objectives, educational formats and talent application, educational content and practical needs, as well as promotion goals and methods, the article discusses the issues currently existing in IP information literacy education in universities against the backdrop of new quality productivity, and proposes corresponding strategies. [Results/Conclusions] New quality productivity is driving the integrated upward development of various industries. As the main institution for IP information literacy education, libraries should seize the development opportunities, cultivate forward-looking new quality intellectual property talented people, continuously strengthen their IP information literacy teaching ability by analyzing their own weaknesses, They should grasp the current wave of emerging technologies, enhance human resources development, constantly innovate educational concepts, innovate service models, attract multiple entities to participate in building an industry-university-research integration community, and thereby promote the high-quality transformation and development of libraries. A limitation of this article is that it only conducts a survey of educational institutions in universities without involving a survey of educational object needs. In subsequent research, a method based primarily on field research will be adopted to expand the scope of the survey.

[Purpose/Significance] Study tours, as a dynamic integration of education and tourism, represent a significant opportunity for public libraries to innovate their service models and enhance their cultural and educational roles within the framework of cultural tourism integration. This study explores the pathways for public libraries to develop high-quality study tours, which addresses the growing demand for diverse, high-quality social education in the context of China's "cultural confidence" and "national reading" initiatives. Unlike prior studies that focus narrowly on specific library practices or regional cases, this research provides a comprehensive analysis by integrating domestic and international perspectives, we emphasize the strategic role of public libraries in cultural resource transformation. This paper contributes to the academic discourse by proposing actionable frameworks for service innovation. These frameworks position public libraries as pivotal players in advancing cultural education and addressing contemporary societal needs through interdisciplinary collaboration and resource optimization. [Method/Process] Adopting a cultural tourism integration approach, this study employs a multi-method approach that combines a literature review, a comparative case analysis, and an empirical survey to examine the current state of, and challenges to, public library study tour services. The research establishes a theoretical foundation by drawing on policy documents, industry reports, and academic literature to establish a theoretical foundation, and identifies best practices and gaps by analyzing representative domestic and international library cases. Domestic cases span provincial, municipal, and county-level libraries, covering diverse regions and service models, such as digital innovation and regional cultural integration. International cases include libraries in North America, Europe, and Asia, highlighting technological applications and cross-sector collaboration. The comparative analysis focuses on cooperation models, course design, and service mechanisms, supported by empirical data from user feedback and activity records. This approach ensures a robust understanding of practical challenges and opportunities, grounded in both national policy contexts and global experiences. [Results/Conclusions] The study identified key challenges in public library study tour services, including insufficient resource integration, severe homogenization of service formats, lack of evaluation systems, and weak cross-sector collaboration mechanisms. To address these issues, five strategic pathways have been proposed: establishing multi-party collaboration frameworks involving government, schools, and social organizations; creating expert talent pools to enhance service professionalism; developing standardized service guidelines to improve consistency and quality; deepening thematic content by leveraging library collections; and implementing comprehensive feedback and evaluation systems to ensure continuous improvement. These strategies enable public libraries to create distinctive, culturally rich study tour programs that align with regional identities and educational goals. However, there are limitations, such as the scalability of resource-intensive models and the need for ongoing funding. Future research could explore the integration of digital technology, such as AI-driven evaluation systems, and cross-regional collaboration to enhance scalability and inclusivity of public libraries, thereby advancing their role in cultural tourism integration and social education.

[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] As generative artificial intelligence (AI) transforms library services, existing evaluation systems fail to capture dynamic characteristics of AI-driven resource discovery. This study develops a dynamic evaluation framework for public libraries' AI-enhanced services, addressing the gap between technological innovation and service assessment. [Method/Process] The research employed a mixed-methods approach to develop and verify a multi-dimensional evaluation framework based on Knowledge Organization Systems (KOS) theory. The framework comprises five primary dimensions: physical environment, technical architecture, content organization, user interaction, and innovation capability-operationalized through fifteen secondary indicators. Each indicator was carefully designed to capture AI-specific capabilities, including cognitive guidance efficiency, multimodal interaction precision, semantic network depth, and generation-enhanced utilization rate. A sophisticated hybrid weighting methodology was implemented, integrating subjective and objective approaches. For subjective weights, the Analytic Hierarchy Process was employed with 30 domain experts constructing pairwise comparison matrices using standardized scaling methods. Geometric mean aggregation was applied to synthesize individual judgments, with consistency ratios maintained below the threshold to ensure logical coherence. For objective weights, the entropy method analyzed actual evaluation data variance, with greater variance indicating higher discriminatory power. The final weights were derived through multiplicative synthesis combining both approaches. The empirical validation study involved collecting 492 valid questionnaires from 14 strategically selected public libraries representing different stages of AI implementation between September and November 2024: one municipal library with comprehensive AI deployment, 11 district libraries with partial implementation, and 2 county libraries in early adoption phases. The questionnaire utilized a five-point Likert scale to assess real-time service performance across multiple scenarios. Statistical analysis employed fuzzy comprehensive evaluation to handle uncertainty in subjective assessments, structural equation modeling to validate construct relationships, and latent class analysis to identify distinct user interaction patterns. The framework demonstrated high reliability with Cronbach's alpha reaching 0.845 and strong construct validity with KMO value of 0.873. [Results/Conclusions] Content organization emerged as the most critical dimension with a combined weight of 0.302 2, while semantic network depth, cognitive guidance efficiency, and cross-media consistency ranked as top secondary indicators with weights of 0.090 3, 0.086 1, and 0.084 7 respectively. Performance evaluation revealed content organization scoring 74.873 points versus user interaction at 68.040 points, highlighting the gap between technical capabilities and user experience. Significant differences existed across library levels, with municipal libraries outperforming county libraries by over one point in technical architecture and semantic network depth. Four distinct user patterns emerged: technology-oriented, content-immersive, efficiency-focused, and assistance-dependent. Each requires a tailored service approach. The study proposes the following optimization strategies: multimodal interaction frameworks, adaptive user profiling, hierarchical collaboration mechanisms, and knowledge graph-based content reorganization.

[Purpose/Significance] Currently, large language models (LLMs) and agents have emerged as core technical paradigms in artificial intelligence, with their integration into scientific research scenarios holding profound significance for transforming research paradigms. Traditional scientific research is facing an increasing number of challenges such as inefficient literature searches, the processing of massive amounts of data, repetitive experimental tasks, and barriers to collaborative innovation. Agents, empowered by LLMs, offer a promising solution to these bottlenecks by enabling intelligent automation and adaptive collaboration across research workflows. Beyond basic task assistance, they play a pivotal role in facilitating knowledge fusion, accelerating breakthroughs in frontier areas, and reshaping traditional research models. This study aims to clarify the core techniques and applications of agents in scientific research, highlighting their transition from auxiliary tools to integral innovation partners, which is crucial for accelerating knowledge discovery, enhancing research efficiency, and promoting the shift toward intelligent and collaborative research models. [Method/Process] Employing an objective, inductive approach, this study thoroughly explains the core technical modules of agents including planning, perception, action, and memory, as well as the operational mechanisms of multi-agent collaboration. It also integrates an analysis of agent applications throughout the entire scientific research lifecycle. This analysis covers key scenarios including literature review and idea formulation, experimental planning and design, data processing and execution, result analysis and knowledge discovery, and research report composition. By analyzing the application value and existing limitations of agents, this study proposes prospects and recommendations for the application and development of agents in scientific research scenarios. [Results/Conclusions The findings reveal that LLM-driven agents are evolving from basic task executors to active participants in scientific discovery, demonstrating significant transformative potential throughout the entire research workflow. They facilitate more efficient information processing, smarter experimental design, and deeper knowledge integration, thereby redefining traditional research patterns. However, several challenges persist, including limitations in long-range reasoning capabilities, and underdeveloped ecosystem support. There are also ethical and security concerns, such as data privacy and academic integrity. To address these, future efforts should focus on strengthening intelligent computing infrastructure for scientific data, deepening collaborative development of domain-specific agents, establishing a unified open collaboration framework with standardized interfaces, and building "human-in-the-loop" hybrid systems and multiple evaluation mechanisms. These measures will enable agents to become core partners in scientific innovation, driving the transition of research paradigms toward greater intelligence and collaboration.

[Purpose/Significance] This study aims to explore the reading difficulties experienced by children in intergenerational caregiving situations in rural China, analyze the causes of these difficulties, and propose targeted solutions. The research is motivated by the growing concerns about educational disparities and developmental challenges experienced by this vulnerable group, especially within the context of China's rural revitalization strategy. Unlike previous studies, which have primarily focused on the broader category of rural left-behind children, this paper focuses on a specific subgroup－rural children raised by their grandparents - to offer a more nuanced understanding of the unique obstacles these children face in relation to reading. This study contributes to both academic discourse on rural education and efforts aimed at promoting equitable development by identifying the structural and cultural factors that contribute to low reading literacy among these children. By integrating theories of family sociology, educational inequality, and digital divide, it fills a critical gap in existing literature and offers new insights into how intergenerational caregiving intersects with literacy development. [Method/Process] The research was conducted in a rural county located in Guangdong Province. A mixed-methods approach was adopted that combined in-depth interviews with caregivers and teachers, a textual analysis of local education policies, and online surveys of rural schools and community centers. A grounded theory approach was employed as the analytical framework, and a three-stage coding process was used to develop a measurement model for assessing individual reading barriers. This methodological rigor ensured that the findings were grounded in empirical data, yet still allowed for theoretical generalization. [Results/Conclusions] The findings reveal that rural children under intergenerational care face multiple reading challenges, including limited access to books, inadequate reading environments, and a lack of awareness about the importance of reading. These issues stem from complex sociostructural factors, including fragmented family structures, limited educational opportunities for grandparents, and imbalanced use of digital technologies. To address these challenges, the study proposes a multi-pronged intervention framework. This framework includes strengthening policy support for rural reading programs, mobilizing volunteers as reading mentors, guiding the appropriate use of digital tools to enhance literacy, and encouraging intergenerational reading activities within families. While this study provides valuable insights, further longitudinal and comparative research across diverse rural regions is needed to validate and expand upon these findings. Future studies could also examine the long-term impact of reading interventions on children's academic achievement and psychosocial development.

[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.

[Purpose/Significance] This paper focuses on the development process and service mechanisms of Japan's Research Data Cloud (RDC) system, a core national infrastructure coordinated by the Research Center for Open Science and Data Platform (RCOS). Against the backdrop of growing global attention to open science, RDC presents a practical model for integrating data management, open sharing, publication, search, and preservation throughout the research lifecycle. The paper highlights the unique collaborative model of RDC, which is characterized by a small team driving large networks. Compared to prior literature that often emphasizes technical architectures or isolated institutional efforts, the paper situates RDC within Japan's broader open science strategy, offering both theoretical and practical insights. It explores how RDC contributes to advancing the FAIR data principles, supporting cross-sector innovation, and strengthening national science and technology governance. The analysis also offers strategic lessons for China in building a sustainable and service-oriented research data system. [Method/Process] Using a qualitative case study approach, the paper draws on a combination of primary and secondary sources, including official reports, project documentation, and academic literature, and publicly available platform data related to the RDC initiative. It systematically analyzes the organizational structure and collaborative mechanisms of RDC, focusing on the institutional roles, platform components (GakuNin RDM, WEKO3, CiNii Research), and key technological innovations such as data governance, data provenance, secure computing, and trusted storage. In particular, it analyzes how RCOS functions as a neutral coordinator that bridges stakeholders across ministries, universities, and research organizations, and how it plays a role in translating policy mandates into technical services, integrating institutional workflows, and fostering community participation in the open science ecosystem. [Results/Conclusions] Despite constrained resources, RDC has developed a comprehensive research data ecosystem that serves researchers, data managers, librarians, industry, and the public. Japan's experience demonstrates that emphasizing interoperability, governance coordination, and capacity building, especially through small-scale research teams and nationwide collaborative networks, can effectively support the development of robust research infrastructure. The paper concludes by proposing several recommendations for China: the creation of independent coordination agencies to avoid fragmented development, the establishment of standardized service frameworks to enhance interoperability, and the implementation of tiered training programs to improve data literacy and management capacity across disciplines. Future research should further explore comparative institutional models, examine the long-term sustainability of open science ecosystems under different governance conditions, and investigate the cultural, legal, and technical dimensions that shape localized approaches to research data governance.

[Purpose/Significance] Digital literacy education has become the new educational mission of university libraries. Clarifying the user's perception and utilization mechanism of digital literacy knowledge and optimizing the representation of digital literacy knowledge can promote university libraries to achieve satisfying results in digital literacy education. Based on the frontier of representation theory, this study innovatively puts forward the concept of "sensory digital literacy education", constructs a three-dimensional knowledge perception model including action, image and symbolic representation, and reveals the mechanism of digital literacy knowledge representation and user perception behavior through empirical research. It provides a theoretical anchor for the paradigm shift in library education from tool skills training to cognitive skills training. The "cognition-practice-innovation" teaching system and the "three-in-one" resource construction framework proposed in the study effectively connect the knowledge representation theory with the educational practice scene, and provide a viable way for the three-dimensional implementation of digital literacy education in colleges and universities. [Method/Process] Based on the theories of SOR, TAM and self-efficacy, the theoretical hypothesis model of users' perception and utilization of digital literacy knowledge from the perspective of representation was constructed, and was empirically verified by questionnaire and empirical study. [Results/Conclusions] Action representation, reflexive representation and symbolic representation of digital literacy knowledge all positively affect users' perceived ease of use and perceived usefulness of digital literacy knowledge; perceived ease of use has a positive impact on perceived usefulness; self-efficacy plays a positive moderating role between perceived ease of use, perceived usefulness, and user intention and behavior. Due to the limitations of space and personal energy, the shortcomings of this paper are as follows. First, the methodological level is mainly based on quantitative analysis, and the mining of qualitative dimensions such as details of teacher-student interaction and informal learning scenarios in digital literacy education is insufficient. Secondly, the research object focuses on the groups of teachers and students in colleges and universities, and the issues such as the intergenerational differences of the public's digital literacy and the professional digital literacy needs of professionals have not been covered, and the comparative study of multiple subjects can be expanded in the future. In the future, more research can be done on research methods and research objects. Through the deep coupling of representation theory and educational practice, it is expected to provide a new theoretical mirror for the cultivation of cognitive ability in the digital age, and help to build a three-dimensional educational ecology of "technology empowerment-cognitive development-literacy transfer".

[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] This paper aims to provide evidence synthesis crowdsourcing initiators with references to understand user participation behavior, propose and implement relevant behavioral intervention strategies, and guide and promote user participation. [Method/Process] A model of influencing factors of user participation intention was constructed based on the planned behavior theory (TPB), the technology acceptance model (TAM) and motivation theory. Hypotheses were also proposed. The relevant data of the sample objects were collected through a questionnaire survey, and the hypotheses were tested using a structural equation model. [Results/Conclusions] Evidence synthesis crowdsourcing itself is an academic, scientific and non-commercial task. In crowdsourced evidence synthesis, factors such as attitude, self-efficacy and trust significantly impact users' willingness to participate. The academic atmosphere has no significant impact on users' willingness to participate. Monetary rewards, recognition, and skill improvement have a significant positive impact on attitude, while perceived effort has a significant negative impact on attitude, and enjoyment of the activity has no significant impact on attitude. Attitude, self-efficacy, and trust directly influence willingness to participate, while monetary rewards, recognition, skill improvement and perceived effort indirectly influence willingness to participate through attitude. The order of influence of individual motivational factors on willingness to participate, from greatest to least, is as follows: skill improvement, recognition, and monetary rewards. The results show that, compared with monetary rewards, non-material factors are a more important driving force for users to participate in crowdsourced evidence synthesis. In terms of perceived behavioral control factors, efficacy is one of the main cognitive forces that guides users' willingness to participate. The degree of trust directly determines whether users are willing to learn about and participate in the project. Therefore, when organizing crowdsourcing activities and recruiting participants, as well as providing incentive measures, the crowdsourcing party should consider the following aspects: for potential target groups, online training opportunities, development of related learning resources, establishment of interactive feedback mechanisms, and an online community for timely communication can be provided. Participants can be provided with the opportunity to receive signatures, certificates or emails expressing appreciation and encouragement or other forms of recognition. Organizers can provide resources and support to reduce users' perceived burden. This can be done through examples such as organizing training and establishing communities. According to specific circumstances, appropriate monetary rewards can be provided to participants. Organizers should adopt strategies that improve users' self-efficacy, convey relevant information and provide assistance. To gain the trust of users and make appropriate commitments to protect their legitimate rights and interests, organizers are encouraged to provide detailed information about themselves, including the affiliation, research experience of the team and their academic achievements.

[Purpose/Significance] The social application of technologies, such as generative artificial intelligence (AI), presents challenges for adolescents when it comes to deep reading. In response, China has promoted the Youth Reading Initiative and issued the "Notice on Further Implementing the National Youth Student Reading Action", which outlines five key projects and provides a clear roadmap for action. To thoroughly implement and effectively achieve the goals, tt is essential to clarify the specific path choices and their inherent rationality. vMethod/Process] This paper reviews China's decade-long initiatives to develop a youth reading ecosystem. It demonstrates that the nation has established a robust foundation to support "holistic reading" initiatives and prioritizes creating such environments as its strategic focus. The "Notice on Further Implementing the National Youth Student Reading Initiative" first mentioned both the concepts of "holistic reading" and "deep reading" simultaneously. Thus, in this paper, we first clarified four characteristics of holistic reading, and then analyzed the relationship between "holistic reading" and "deep reading" based on discussions about the real-world impact of the AI era on youth reading. We finally elucidated the logical and practical foundations for the formation of current youth reading promotion pathways in China. [Results/Conclusions] China's recent policies for youth reading initiatives demonstrate the nation's commitment to a "holistic reading" approach that encourages "deep reading" among adolescents. Emerging from the interplay between contemporary educational philosophies and evolving educational environments, this strategic choice signifies a return to the fundamental essence of reading. Cultivating reading can comprehensively enhance teenagers independent thinking, social responsibility, innovative spirit, and practical abilities. However, the development of deep reading skills among today's youth faces unprecedented challenges due to significant changes in media environments and knowledge acquisition methods. Therefore, in this era where technological environments profoundly reshape learning conditions, only by embracing the concept of "holistic reading" can teenagers develop the internal motivation needed to counteract the effects of the current information environment and cultivate their perseverance in deep reading. The progression from "holistic reading" to "deep reading" represents a significant shift from reading habits to reading competence. First, broadening one's reading scope lays the foundation for deep reading. Second, access to quality reading materials ensures effective outcomes of deep reading. Third, peer motivation cultivates the drive for deep reading. Fourth, promoting specialized reading creates societal momentum that propels deeper engagement. Finally, the paper posits that achieving this transformation necessitates coordinated efforts spanning various dimensions, including stakeholder engagement, goal-setting, and resource allocation.

[Purpose/Significance] In cross-domain natural language processing (NLP) tasks, deep learning models often exhibit performance variations due to texts with distinct domain characteristics, leading to a decline in model generalization capabilities. Text complexity stands out as one of the most explanatory factors influencing model generalization. [Method/Process] This paper presents two innovative contributions. First, a multi-dimensional text complexity calculation framework grounded in systemic functional linguistics theory was constructed. This framework employs a hierarchical quantification approach: at the lexical level, it dynamically identified four types of non-standard expressions - abbreviations, emoticons, internet buzzwords, and alphanumeric mixed words - and calculated a normative score using a non-linear formula. At the sentence level, an innovative inverse fusion enhancement method (IFEM) was proposed, integrating punctuation anomaly density (weight 0.1), colloquial word ratio (weight 0.4), semantic ambiguity (weight 0.2), and sentence length features (weight 0.3), and generating a structural score through modeling of feature synergy and suppression effects along with an adaptive weighting mechanism. Finally, at the corpus level, a weighted fusion output the global corpus complexity assessment. Experimental results demonstrated that this framework successfully quantifies intrinsic differences between domain texts. For instance, the measured complexity of the waimai_10k dataset reached 0.703, significantly higher than the 0.552 of the ChnSentiCorp_htl_all dataset, and it accurately captured complexity changes even after internal text reduction and substitution operations. Second, a knowledge base-enhanced dynamic adaptive CNN-BiLSTM model was designed. This model implemented the following innovative mechanisms: 1) The knowledge base adopts a dual mapping architecture of text-label and vector-label, supporting historical experience knowledge loading and real-time error recording; 2) Feature weights were adjusted based on the knowledge base content, such as strengthening positive semantic representations or weakening negative expressions. The model architecture integrated multi-scale CNN convolutional kernels for local feature extraction, bidirectional long short-term memory networks for capturing long-distance dependencies, and an attention mechanism to focus on key information. To validate the effectiveness of the proposed methods, experiments were conducted on four Chinese datasets. [Results/Conclusions] The results indicate that the complexity calculation framework exhibits strong robustness, with complexity fluctuations below 3.3% after a 20% sample reduction, and a maximum complexity increase of 13.8% upon short text data injection. Moreover, the framework effectively quantifies and differentiates text complexities, as evidenced by the 0.703 complexity of the waimai_10k dataset compared to the 0.552 of the ChnSentiCorp_htl_all dataset. Additionally, the proposed model demonstrated optimal performance across both the most standardized ChnSentiCorp_htl_all dataset and the most challenging waimai_10k dataset (achieving accuracies of 0.923 8 and 0.943 4, respectively), significantly outperforming Transformer and various large language models such as deepseek-v3 and qwen-plus.

[Purpose/Significance] Satisfaction is the patient's evaluation and emotional feedback on the entire mobile healthcare experience. Not only does it directly affect the patient's experience, but it also significantly influences user adoption and retention. Therefore, this study aims to explore the influencing factors, hierarchical relationships, and associated pathways of user satisfaction with mobile health applications, and provide scientific evidence and practical recommendations for the healthy development of mobile health applications, thereby promoting the construction of a healthy China and intelligent healthcare. By clarifying the key drivers of user satisfaction and their interactions, the study provides theoretical support for enhancing user experience, optimizing service quality, and increasing user retention. [Method/Process] This study first crawled, cleaned, and filtered negative user review data from mobile health applications, resulting in 539 valid data points after processing. Using the grounded theory, the study extracted factors influencing user satisfaction with mobile health applications by coding the review data. Subsequently, based on the interpretive structural model (ISM), the internal logic and associated pathways between these influencing factors were explored. Finally, the cross-impact matrix multiplication (MICMAC) method was used to examine the dependencies and driving forces among the influencing factors, and to identify the key factors affecting user satisfaction with mobile health applications. [Results/Conclusions] The study found that user satisfaction with mobile health applications is influenced by 23 factors across eight dimensions, including physician service quality, management service quality, system quality, information quality, transaction quality, perceived value, perceived risk, and perceived cost. Perceived cost and perceived risk are key drivers that directly affect user satisfaction. The middle-level factors transmit the effects of the bottom-level factors to the top level, acting as "mediators," and consist of factors from the dimensions of system quality, information quality, perceived value, transaction quality, and perceived risk. The bottom-level factors are the primary driving forces, including the quality of medical service, management service quality, system quality, and information quality. Based on the analysis results, this study proposes the following practical recommendations: strictly review the qualifications of doctors and establish a service quality evaluation mechanism; provide communication training for doctors and simplify medical terminology; add artificial intelligence and human services, and regularly train management service staff; design a simple interface and offer personalized customization; ensure information security and privacy, follow the principle of minimal data collection, and allow users to view and delete their personal information. Subsequent research, based on the expansion of the types of mobile health applications, will use a combination of qualitative and quantitative research methods to explore more deeply the relationships among the various factors that influence user satisfaction.