Abstract:

[Purpose/Significance] Generative artificial intelligence, big data, knowledge graphs, and other smart digital technologies are being rapidly integrated into university library resource development, subject services, information literacy education, and smart consultation, shifting service models from a purely resource-supply approach to one focused on knowledge discovery, data support, and intelligent collaboration. Librarian skill development is also moving from general knowledge updates to continuous capability building centered on job tasks, service scenarios, technology applications, and organizational collaboration. In reality, some university libraries still face issues such as training content being disconnected from positions, a gap between technology learning and service application, unclear job roles, insufficient cross-department collaboration, and underutilization of communities. Based on this, this study examines how organizational support systems-through rules, tools, communities, and coordinated division of labor-promote the development of librarians' smart digital skills and their application in services. The research topic stems from the practical tension between "rapid technology iteration" and "relatively lagging librarian skill updates" during digital transformation. Unlike previous studies that focus on individual learning motivation, skill components, or training models, this paper places librarian learning within the organizational activity system, revealing the interactions between job tasks, technical tools, institutional rules, learning communities, and project division. It proposes a skill-building path of "job task guidance-community collaboration-tool adaptation-rule feedback," aiming to shift related research from static description to dynamic mechanism analysis, and provides a basis for university libraries to respond to technological substitution, role redefinition, and service upgrading. [Method/Process] This study uses activity theory as the analytical framework and regards librarians as activity subjects, intelligent platforms, digital resources, and training tools as mediating tools, training policies, job norms, and performance evaluations as rules, and librarian teams, users, teaching staff, and technical departments as communities. It also uses job division to analyze the process of turning learning outcomes into service practice. Using purposive sampling, three different types of university libraries were selected, and semi-structured interviews were conducted with 18 librarians responsible for resource development, subject services, information literacy education, technical support, and overall management. Organizational documents such as training policies, project records, and job descriptions were also collected for triangulation. Data analysis followed familiarization, open coding, theme generation, theme review, and theoretical integration. This method is suitable for revealing interactive mechanisms and action logic within organizational contexts. This study is exploratory and qualitative in nature, without performing related statistical analysis, significance testing, or general causal inference. [Results/Conclusions] The study found that librarians' digital intelligence is not just about personal willingness or one-time training; it is shaped by job tasks, organizational rules, digital tools, learning communities, and project division together. Job tasks can enhance the sense of learning goals; adapting to rules and tools can boost learning motivation and efficiency; communities help calibrate learning direction through feedback, resource sharing, and peer evaluation; proper division of labor helps leverage the complementary strengths of technical and service-oriented librarians, easing the mismatch between tech supply and service demand; conflicts between old rules and new tasks drive institutional adjustments and updates to the skills system. It is recommended to set up tiered training aligned with job tasks, build cross-departmental learning communities, improve trial and feedback mechanisms for digital tools, and include knowledge sharing, project outcomes, and service innovation in performance evaluations. Limitations of the study include a restricted sample and regional coverage, mainly relying on interviews and organizational documents. In the future, the sample could be expanded, combining surveys, behavioral data, and longitudinal cases to further explore AI ethics, librarian professional identity, human-machine collaboration boundaries, and digital intelligence assessment standards.

Key words: digital intelligence era, university librarians, lifelong learning, organizational support system, activity theory