Abstract:

[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.

Key words: generative artificial intelligence, digital library, information literacy education, the role of a librarian