关键词: 高校图书馆, AI素养, 复合型AI馆员, 核心能力模型, DeepSeek平台, 人工智能技术

Abstract:

[Purpose/Significance] With the rapid advancement of artificial intelligence (AI), university libraries are undergoing a deep transformation from traditional resource repositories to intelligent service ecosystems. This transformation poses a significant challenge to the conventional competencies of librarians and underscores the necessity for a systematic reconstruction of these competencies. Existing studies often lack empirically supported and integrative models, and they seldom bridge the gap between AI application and competence development. To address these shortcomings, this study proposes a core competence model for hybrid AI librarians, integrating technical, service, and management dimensions. The research highlights its innovation by not only theorizing but also empirically validating the model through grounded data, positioning the study as a meaningful contribution to the discourse on digital librarianship. Different from previous literature, it integrates AI platform practices within the competency framework. This integration serves to enrich both theoretical underpinnings and enhance the practical applicability of the theory. This provides actionable implications for the sustainable development of librarianship in the context of national strategies for digital transformation and technological innovation. [Method/Process] The study employed a mixed-methods approach. First, a literature review was conducted to analyze trends in AI applications within university libraries. Then, semi-structured in-depth interviews were carried out with ten librarians from multiple universities that have deployed the DeepSeek intelligent platform. The participants covered technical, service, and management positions, with more than three years of experience using AI tools and a distribution across middle to senior professional titles. Following data collection, the grounded theory was applied with three levels of coding (open, axial, and selective) to inductively derive categories and explore how technical, service, and management competencies interact. The principle of data saturation was strictly observed to ensure methodological rigor, and no additional categories emerged after the three competency domains were established. [Results/ [Conclusions] Findings indicate that the core competencies of hybrid AI librarians revolve around three interdependent domains. Technical competence involves intelligent tool operation, data analysis, and system maintenance, supporting the integration of AI into daily workflows. Service competence emphasizes user-centered design, personalized recommendation, and human-AI collaborative interaction, ensuring that technical functions translate into user value. Management competence addresses resource allocation, cross-department collaboration, and ethical governance, safeguarding sustainability, compliance, and innovation. Together, these dimensions form a "technology-service-management" dynamic balance model, characterized by reinforcing loops in which technology drives service, service demands managerial support, and management stabilizes technology-service integration. Furthermore, a training and cultivation framework was proposed, offering differentiated professional pathways based on librarians' roles and growth stages. The study concluded that such a model not only enhances service effectiveness but also contributes to national innovation strategies. The study's limitations include its scope, which is limited to a single country and a small sample size. Future research should expand the sample base, employ comparative studies across institutions, and further examine the weighting of competencies.

Key words: university library, AI literacy, compound AI librarian, core competence model, deepSeek platform, artificial intelligence technology