Automated learning support literature classification using large language models via different strategies: a study of the LIS literature

Purpose The purpose of this study is to propose a literature classification scheme based on its knowledge type adapted to search as learning scenario, and explore the feasibility of using generative artificial intelligence tools to automatically complete this kind of literature classification. Design/methodology/approach This study mainly includes two parts: (1) this study investigates knowledge classification from the cognitive perspective, then models the knowledge learning process during academic search based on constructivism, and finally proposes the learning support literature classification (LSLC). (2) Based on three open source large language models (DeepSeek-R1-Distill-Owen-7B, LLaMA3.1-8B and Qwen2.5-7B), this study designs a two-stage experiment of single strategies and hybrid strategies. The classification task performance of three large language models under six different strategies is compared and analysed. Findings This study proposes the LSLC. The first-level classification includes four categories of declarative, procedural, deepened and related content. The second-level classification includes 14 categories of literature review, overview research and so on. Then, six strategies are designed to improve large language models’ performance to auto-complete this kind of literature classification. LLaMA-3.1-8B performs best after optimization. For Chinese literature, the F1 values of first-level and second-level classification of fine-tuned LLaMA-3.1-8B are 88.05% and 71.43%, respectively. For English literature, the F1 values of first-level and second-level classification of fine-tuned and simple thinking prompted LLaMA-3.1-8B are 75.26% and 65%, respectively. Research limitations/implications This study proposes a theoretical achievement of LSLC, and verifies that it is feasible to automatically complete literature classification from a cognitive perspective using large language model, which supports the conclusion that generative artificial intelligence can effectively assist social science research. Originality/value This study proposes a theoretical achievement of LSLC and verifies that it is feasible to automatically complete literature classification from a cognitive perspective using a large language model, which supports the conclusion that generative artificial intelligence can effectively assist social science research.