Abstract:

[Purpose/Significance] With the rapid advancement of information technology, library services are undergoing transformative changes. The emergence of generative artificial intelligence (Generative AI) presents unprecedented opportunities and challenges for innovation in smart library services. By enhancing service efficiency and user experience, generative AI supports core library functions, such as personalized recommendations, intelligent question answering, and automatic summarization. This research explores the implications of applying generative AI technology to library services, with the goal of understanding its transformative impact on the field and addressing its potential risks. Unlike traditional studies that focus primarily on functionality, this study emphasizes the ethical, technical, and management risks associated with the use of generative AI in libraries. The study occupies an important place in the advancement of knowledge in this area and contributes to the development of sustainable, user-centered library services capable of addressing significant contemporary challenges related to information accessibility and data security. [Method/Process] This study uses a systematic literature review and case analysis to examine the current state of generative AI applications in smart libraries. A comprehensive approach is taken to understand how generative AI can enhance library services in areas such as personalized recommendation systems, intelligent Q&A, and automated summarization. The study draws on both theoretical and empirical sources, utilizing qualitative analysis to examine trends in the use of generative AI in different types of library services. This review also includes a thorough examination of the potential risks associated with implementing these technologies. Technical risks include data security vulnerabilities and model bias, while ethical risks focus on the issues surrounding user privacy, misinformation, and intellectual property rights. Management risks are also discussed, including the challenges of maintaining system stability and ensuring regulatory compliance. The multi-dimensional risk framework developed in this study provides a robust structure for analyzing these complex challenges and serves as a foundation for future empirical research in smart library applications. [Results/Conclusions] The research reveals that while generative AI can significantly improve the quality of library services and user satisfaction, it also poses significant risks. These include challenges related to data security, model bias, ethical standards, and management complexity. To address these, the study proposes a number of risk mitigation strategies. Key recommendations include strengthening data security through advanced encryption and access controls, increasing model transparency to build user confidence, and ensuring system stability through rigorous testing and monitoring. In addition, the study advocates for the establishment of ethical guidelines that prioritize user privacy, transparency, and content accuracy. It also underscores the need for ongoing regulatory adjustments to keep pace with technological advances. The study concludes by identifying limitations, such as the lack of quantitative data and real-time experiments, and suggests areas for future research. Future studies should focus on empirically validating the proposed framework, exploring the long-term impact of generative AI on library services, and developing best practices for balancing innovation with ethical responsibility. The continued evolution of generative AI is likely to deepen its integration with smart libraries, enabling innovative service models that meet the diverse and dynamic needs of users while safeguarding against potential risks. This research provides a foundational reference for library managers and policymakers seeking to implement generative AI responsibly and sustainably, and to promote the progressive transformation of library services in the information age.

Key words: personalized recommendation service, generative artificial intelligence, smart library, user satisfaction, data analysis