Abstract:

[Purpose/Significance] Against the backdrop of national innovation-driven development strategies and the pressing need to enhance the efficiency with which scientific and technological achievements are transformed within universities, university libraries are undergoing a critical transition. They are shifting from being traditional, passive information providers to becoming proactive, embedded partners in the research and innovation value chain. However, this transition is often hampered by inherent limitations in traditional service models. This study, therefore, posits artificial intelligence (AI) as a pivotal enabler and investigates the specific mechanisms through which AI technologies can empower university libraries to achieve deep, systemic integration into the entire lifecycle of technology transfer. The research aims to provide a comprehensive theoretical framework for understanding this transformation and offer actionable, evidence-based practical pathways for academic libraries to redefine their functional boundaries and substantially strengthen the institutional support ecosystem for university technology transfer. [Method/Process] This research employs a qualitative multi-case study design, underpinned by an analytical framework constructed around the four critical, sequential stages of the technology transfer lifecycle: 1) research topic selection and project initiation, 2) research and development, 3) project conclusion and evaluation, and 4) marketization and industrialization of outcomes. Case selection followed purposive sampling criteria to ensure representation across diverse contexts, including domestic and international universities, as well as varied library types. The primary data comprised detailed case descriptions from published academic literature, institutional reports, and official service platforms. Within this staged framework, the analysis focuses on two intertwined dimensions at each phase: the evolution of the library's core service functions and the transformative impact of AI empowerment. Through a comparative cross-case analysis, this study examines how specific AI technologies augment traditional services, fundamentally changing the role and value proposition of libraries. [Results/Conclusions] The results show that through intelligent information analysis, knowledge association, data mining, and precise matching, AI can promote university libraries to shift from resource supply-oriented support to collaborative services that run through the entire lifecycle of technology transfer. This transformation manifests across the four-stage lifecycle as a shift: from providing literature to forecasting opportunities at the initiation phase; from offering patent data to navigating R&D pathways and risks during development; from archiving outputs to assessing value and potential at conclusion; and from disseminating information to intelligently brokering industry partnerships at the commercialization phase. Synthesizing these stage-specific transformations, this study constructs a novel, integrated service framework. This framework explicitly links specific AI capabilities with the redefined core functions of the library at each stage, illustrating the transition from a linear support model to a dynamic, AI-augmented ecosystem wherein the library serves as a central intelligence node. Meanwhile, this study reveals practical challenges in current practices, including ambiguous organizational boundaries, insufficient professional capabilities, and imperfect evaluation mechanisms oriented toward technology transfer. Correspondingly, it proposes strategies such as clarifying collaborative positioning, strengthening the construction of AI-empowered service capabilities, and improving technology transfer-oriented evaluation mechanisms to promote the sustainable development of AI-empowered research services in university libraries.

Key words: artificial intelligence, university libraries, technology transfer, full-lifecycle services, case study