Abstract:

[Purpose/Significance] This study investigates the operational practices and strategic development pathways of intelligent consultation services in libraries globally, specifically under the impetus of artificial intelligence (AI) large language models (LLMs). By conducting a systematic analysis of representative case studies, we examine the applied technologies, emerging service models, and measurable efficacy of these AI-enhanced services. The research holds significance in offering actionable insights for the effective implementation of AI within the library sector. It aims to guide the evolution of intelligent consultation toward greater innovation and cultural-contextual adaptability, thereby providing both theoretical underpinning and practical guidance for the localized development of smart library ecosystems. [Method/Process] Employing a comparative case study methodology, this research selected 30 representative libraries from diverse international and domestic contexts as its subjects. Data were primarily gathered through structured online surveys and content analysis of publicly available service interfaces, systematically capturing the scope, functionality, and operational status of their intelligent consultation services. The analysis focused on characterizing technological applications-identifying core LLM integrations, typical functionalities, and architectural highlights. It further integrated findings to compare and contrast prevailing service models and implementation variances. Subsequently, the study conducted a multidimensional comparative assessment of the practical service effectiveness enabled by AI large models, evaluating performance across four key areas: service response efficiency and accuracy; capabilities in resource organization and structured knowledge management; tangible improvements in user service experience; and degree of service model innovation. [Results/Conclusions] The findings indicate that AI large model-driven intelligent consulting services exhibit pronounced advantages in key operational metrics, including enhanced response efficiency, superior knowledge synthesis and management capabilities, enriched user interaction experiences, and the facilitation of novel service paradigms. However, a comparative analysis reveals significant disparities among libraries concerning the depth of technological integration, the sophistication of service offerings, and the level of cultural and linguistic adaptation achieved. In response, the study proposes targeted strategic recommendations from three interrelated perspectives: nuanced technological application, user-centered service design, and collaborative ecosystem construction. It advocates for libraries to prioritize the synergistic balance between technological capability and humanistic service values, to achieve deeper integration with localized and institutional knowledge repositories, and to institute mechanisms for continuous service evaluation and iterative optimization. These approaches are essential for fostering more efficient, inclusive, and sustainable development of intelligent consultation services. Future research directions should encompass longitudinal studies on service effectiveness, the integration of multimodal interactive capabilities, and the formulation of ethical guidelines and governance frameworks for AI deployment in library services.

Key words: intelligent consulting services, AI large model-driven, library, efficiency research