[Purpose/Significance] Improving artificial intelligence (AI) literacy has emerged as a critical focus in global education, reflecting the growing significance of AI in today's society. This study aims to explore and interpret the core elements and key competencies articulated in the UNESCO AI Competency Framework for teachers and students, and to provide practical guidance for educators and policymakers, offering insights that can facilitate the systematic integration of AI literacy education. A comprehensive approach to AI education is needed to equip both students and teachers with the skills and knowledge necessary to navigate and thrive in an increasingly intelligent era. The results of this study are intended to support the formulation of effective pedagogical strategies, thereby contributing to the enhancement of AI literacy among educational stakeholders. [Method/Process] The study analyzes the preliminary policy foundations and background that led to the creation of the UNESCO AI Competency Framework. It analyzes the content of both the AI CFS and the AI CFT, focusing on key principles and framework structure to systematically interpret the framework's content. In particular, this study explores the policy context in which these frameworks were developed and examines how global educational goals and technological advances have influenced the articulation of AI competencies. By understanding the development and rationale behind the UNESCO AI Competency Framework, this study aims to provide a comprehensive overview that can support the development of effective AI literacy initiatives. It also highlights the connections between the intentions of the frameworks and the practical competencies required of educators and students, thereby contributing to a deeper understanding of how AI literacy can be meaningfully integrated into educational practice. [ [Results/Conclusions] Based on the experience provided by the competency framework and considering the current state of AI literacy education, this study offers insights and recommendations for developing AI literacy education in China from four perspectives: core values, policy refinement, practical application, and future implementation. Specifically, this study emphasizes that all educational stakeholders should work together to improve AI educational content and methods, and move toward a teacher-student-AI interaction model that empowers teachers, fosters student creativity, and integrates AI as a facilitator of personalized, flexible, and multidirectional learning. In terms of policy refinement, this study advocates for the creation of a supportive policy environment that addresses the unique challenges faced by educators and learners in the Chinese context. For practical application, the study provides actionable recommendations for integrating AI literacy into curricula, emphasizing project-based learning, hands-on experiences, and interdisciplinary approaches that foster a comprehensive understanding of AI concepts. Finally, in terms of future implementation, this study highlights the need for ongoing professional development for educators, such as the establishment of assessment mechanisms to monitor and evaluate the effectiveness of AI literacy programs over time.
[Purpose/Significance] The rapid development of generative artificial intelligence (GenAI) has led to a growing demand for AI literacy in various fields. However, current AI literacy courses often fail to adequately address the diverse needs of students with different academic backgrounds, expertise, and learning levels. This research aims to design an AI literacy curriculum that balances knowledge dissemination with skill development, ensuring that students can not only understand basic concepts but also apply them in practice. [Method/Process] This study is based on the design of Nanjing University's "Exploration of Frontier Applications of Generative Artificial Intelligence" course, which adopts the "knowledge-skills" navigation framework. The course is divided into four progressively advanced levels: foundational cognition, core understanding, tool application, and innovative development. The foundational cognition level systematically organizes the four key knowledge modules involved in generative artificial intelligence: Machine Learning, Neural Networks, Deep Learning, and Natural Language Processing, helping students to build an initial cognitive framework for GenAI. The core understanding level explores advanced topics in GenAI, covering four main modules: basic model pre-training, downstream task adaptation, human-AI value alignment, and AI agents. This aims to enhance students' comprehensive understanding of the technical principles, application methods, and ethical considerations, providing the necessary technical support and conceptual tools for real-world applications. The tool application level consists of three modules: classification of intelligent tools, tool acquisition and use, and derived applications. It gradually guides students from analyzing the characteristics of tools and their use to exploring state-of-the-art applications in multi-modal, multi-scenario, and integrated contexts. Finally, the innovative development level is the final practical stage of the GenAI learning system and includes environment configuration, basic processes, and frontier development. This includes configuration of hardware and software environments, basic steps for development tasks, and advanced practices for complex functions, forming a complete chain from basic support to high-end applications. Following the "knowledge-skills" navigation, the course will also include teaching designs such as concept cognition modules, multi-modal generation and application skill modules, advanced generative AI knowledge and skill modules, and generative AI governance modules, along with the development of corresponding online open courses, open educational resources, and experimental equipment resources. [Results/Conclusions] The "knowledge-skills" navigation framework effectively enhances students' AI literacy by successfully bridging the gap between theoretical knowledge and practical application. The modular structure of the course, combined with multi-modal learning and hands-on practice, effectively meets the diverse learning needs of students. The course allows students to gradually build a knowledge system from basic concepts to advanced skills, fostering a comprehensive understanding of AI technologies.
[Purpose/Significance] With the proliferation of artificial intelligence (AI) technology, knowledge workers' basic understanding of AI and their ability to evaluate its application, known as AI literacy, is particularly important. This study addresses the current state of the AI literacy gap among knowledge workers and explores how AI technology itself can be used to bridge this gap, with the goal of shortening the AI literacy gap. The article analyzes the reasons for the existence of the AI gap in multiple dimensions and the need to bridge the AI gap. In today's era of proliferating AI tools, mastering the use of AI tools and improving AI literacy is the best assistant for knowledge workers. Learning to use AI tools can significantly improve the efficiency of information collection, processing, and storage, and greatly facilitate the building of information resources. [Method/Process] From the dimensions of cognition, practice, and assessment, the study explores how AI technology can assist in enhancing AI literacy. At the cognitive level, the research examines how AI agents can provide personalized knowledge services and help users build a systematic body of knowledge in a particular domain; at the practical level, it analyzes how AI tools can simplify professional tasks such as data analysis, and suggests how to improve knowledge workers' own ways and means of using AI tools through human-computer interaction and mastering effective AI problem-solving aids; at the assessment level, the application of AI tools in verifying the authenticity of information and the importance of the "human-in-the-loop" model in AI applications are discussed, emphasizing the need for human oversight. The article comprehensively draws on the excellent literature on AI, library intelligence, and learning literacy at home and abroad, and combines its own practical situation of using AI tools to achieve AI literacy, which helps knowledge workers establish basic cognition, practical operation ability, and evaluation of answer results. [Results/Conclusions] AI technology has significant advantages in closing the AI literacy gap. AI agents can provide customized knowledge explanation based on user needs, and AI tools can automate the execution of professional tasks and provide factual evidence. These applications not only compensate for the shortcomings of traditional educational methods but also offer new directions for innovation in the field of education. In presenting the arguments, the number of AI tools chosen is small, but fundamentally representative of the problem. Subsequent research will focus on improving AI literacy from other perspectives, and on ways and means to make knowledge workers more comfortable with AI tools. With the continuous development of AI technology, its role in enhancing AI literacy will become more pronounced, contributing to the construction of a more intelligent and efficient information environment.
[Purpose/Significance] AI literacy is becoming increasingly important, not only to adapt to the future development of higher education and the needs of future society, but also to cultivate innovative thinking and problem-solving skills, to enhance decision-making abilities and, most importantly, to emphasize ethical education to avoid the abuse and misuse of AI technology. Existing research emphasizes the importance of AI literacy, with a focus on discussing AI literacy frameworks and pathways. Although some scholars have classified and discussed the AI literacy for teachers and students, there has not been a comprehensive analysis of the skill requirements for different roles in the context of "AI + higher education". [Method/Process] AI literacy education is a multidimensional and multi-level systematic problem. Based on 18 application cases, this study analyzes the specific application scenarios of AI in the educational process, summarizes the development characteristics of "AI+higher education", and analyzes its AI literacy requirements for university teachers, students, managers, and teaching assistants. Therefore, four-role framework for AI literacy is constructed to provide a theoretical reference for future AI literacy education in higher education. [Results/Conclusions] In the context of "AI + Higher Education," future higher education will continue to develop towards ubiquitous teaching, personalized learning, diversified evaluation, and scientific management, ultimately achieving the complete intellectualization of higher education. For teachers, the skills required are innovative teaching and technological integration; for students, active learning and diversified skills; for administrators, forward-thinking leadership and data-driven decision-making; and for educational support staff, intelligent integration of services and resources. The core elements of AI literacy can be summarized as four key components: thinking, knowledge, skills, and attitudes. In specific educational scenarios, the AI skills of teachers, students, administrators, and educational support staff have similarities but also exhibit differences. Due to space limitations, this study did not construct an AI literacy education pathway. In future research, we will continue to deepen the connotation of AI literacy and propose targeted AI literacy education pathways based on the skill requirements of different roles.
[Purpose/Significance] Artificial intelligence-generated content (AIGC) is having a profound impact on the field of education. Currently, there are some problems in the digital education environment, such as incomplete digital infrastructure and slow digital transformation. The postgraduate education system has not yet fully responded to the changes in the educational environment in the intelligent era. In the era of AIGC, digital literacy has become an important component of graduate students' core competence, which is related to their future academic research and career development. In order to promote graduate students from the understanding of intelligent technology to the rational application, this paper explores a new way of talent training to adapt to the development of intelligent technology. Improving graduate students' literacy skills is important for adapting to the new demands of learning and research in the AIGC era. [Method/Process] Through a literature review and case analysis, this study explores the importance of digital literacy education for postgraduate students, and identifies challenges in educational content and teaching methods. Based on the successful experience of international universities, by analyzing the advantages and application scenarios of AIGC technology, combined with the current situation and existing problems of graduate students' digital literacy education, this paper proposes strategies and ways to improve graduate students' digital literacy. Based on the relevant theories of educational technology development, combined with educational practice and case analysis, this paper proposes an improvement plan for postgraduate students' digital literacy education in the AIGC era. [Results/Conclusions] In order to adapt to the changes brought about by AIGC technology, colleges and universities need to innovate in curriculum design, teaching paradigm and evaluation methods, and put forward strategies such as introducing AIGC-related knowledge modules, building interactive digital resources' intelligent recommendation platform, establishing interdisciplinary integration mechanism, strengthening ethical and legal education and establishing supervision mechanism, so as to promote the comprehensive ability of graduate students. Future research can further explore the deep integration path of AIGC technology and postgraduate students' digital literacy education, the high-order thinking practice direction of AIGC to promote digital literacy, and how to give full play to the positive role of AIGC technology in education while ensuring academic integrity. The shortcoming of this study is that because the development of AIGC technology is still in rapid evolution, some of the suggestions of the study may need to be adjusted in time according to the further development of the technology.
[Purpose/Significance] In the rapidly evolving digital landscape, generative artificial intelligence (GenAI) has emerged as a transformative force in information literacy (IL) education, presenting unprecedented opportunities and challenges for library-based learning environments. This scoping review comprehensively examines the integration of GenAI within IL education, moving beyond theoretical frameworks to provide a nuanced analysis of practical applications and strategic implementations. In contrast to existing research that primarily emphasizes technological capabilities, this study explores the profound implications of GenAI on educational paradigms and provides critical insights into the systematic transformation of library IL services in the AI era. [Methods/Process] Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, 51 key literature sources selected from the SSCI, A\&HCI, and CSSCI databases were systematically analyzed. The comprehensive analytical framework encompassed four key dimensions: technology acceptance, educational framework construction, AI literacy cultivation, and the integration of artificial intelligence with IL education. This methodological approach enabled a thorough exploration of current practices while identifying critical gaps in existing research. [Results/Conclusions] The results show that GenAI significantly enhances IL education through personalized learning experiences and improved digital teaching effectiveness. Tools such as ChatGPT have significant potential to promote adaptive learning environments and improve student engagement. The research identifies four primary areas of impact: 1) creating dynamically adaptive learning environments tailored to individual needs, 2) enhancing critical thinking through interactive scenarios, 3) facilitating cross-disciplinary knowledge integration, and 4) generating innovative educational content and resources. However, the study also identifies several critical challenges, including concerns about data accuracy concerns, inherent algorithmic biases, risks to academic integrity, and the potential weakening of independent thinking skills due to over-reliance on AI systems. To address these challenges, the research proposes a comprehensive framework that includes: 1) robust ethical guidelines for the implementation of GenAI, 2) systematic assessment mechanisms to monitor learning outcomes, 3) critical thinking training programs, and 4) strategies to maintain academic integrity and intellectual autonomy. The study emphasizes that the integration of GenAI is more than a technological change - it represents a fundamental shift towards AI literacy education. This evolution will require learners to develop skills beyond traditional IL skills, including understanding AI ethics, legal frameworks, and using AI technologies to solve problems. Future research directions should focus on conducting empirical studies in different educational contexts, developing adaptive teaching frameworks that balance technological innovation with traditional educational values, and investigating the long-term impact of GenAI integration on learning outcomes. By systematically examining the opportunities, challenges, and development trajectories of generative AI, this study provides valuable insights for libraries and educational institutions seeking to optimize their IL programs in the AI era. The findings not only contribute to the theoretical understanding of the role of GenAI in education, but also provdie practical guidance for integrating advanced technologies into traditional educational frameworks, ultimately fostering a more adaptive, intelligent, and personalized learning ecosystem.
[Purpose/Significance] The development of artificial intelligence generated content (AIGC) technology has engendered novel prospects for the establishment of creating inclusive and expansive learning environments. In light of the potential risks associated with the misuse of AIGC tools, the present study analyzes the factors influencing students' use of AIGC tools within the context of artificial intelligence literacy. It constructs a conceptual model framework and explores the relational paths among influencing variables, aiming to provide a theoretical basis for the advancement of AI literacy education in libraries and other educational institutions. [Method/Process] This study adopts a mixed-method approach that primarily integrates Structural Equation Modeling (SEM) and mediation analysis to explore the relationships between the factors that influence AIGC tool usage. A conceptual relationship model was constructed based on the Technology Acceptance Model (TAM), which is widely utilized model for assessing users' acceptance of new technologies. The study builds on this model by adding AI literacy as a key variable to examine its moderating role in shaping the students' use of AIGC tools. The data were collected via a survey disseminated to university students who have used AIGC tools. The survey incorporated a series of inquiries designed to assess constructs such as effort expectancy, performance expectancy, behavioral intention, AI literacy, and actual usage of the tools. The SEM approach was employed to assess the proposed hypotheses and to validate the relationships between the identified factors. Mediation analysis was employed to assess indirect effects between variables. [Results/Conclusions] The findings indicate that effort expectancy exerts a direct impact on the actual use of AIGC tools by students, and indirectly promotes usage behavior through performance expectancy and behavioral intention. Furthermore, AI literacy plays a crucial role in improving the conversion rate from intention to actual usage. Specifically, AI literacy significantly enhances students' acceptance of AIGC tools, especially in terms of increasing their practical ability to use these tools effectively. The research also identifies key factors that influence students' use of AIGC tools, such as performance expectancy, effort expectancy, and behavioral intention, and highlights the significant moderating effect of AI literacy on the relationships among these factors. This study provides empirical evidence for the effective integration of AIGC technology into the education sector and offers theoretical guidance for libraries and educational organizations on how to design AI literacy education programs that help students adapt to a digitally driven society. Future research may encompass a more extensive examination of the utilization of AIGC tools across different academic disciplines, with a particular emphasis on their implementation in specialized domains. Additionally, the proposed model may be refined to better accommodate a wider range of educational contexts and learning scenarios.
