App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Interpretable adaptive learning path recommendation based on large language model

Gong Yonggang
Mo Hongming
Lian Xiaoqin
Li Qiansheng
School of Computer and Artificial Intelligence, Beijing Technology & Business University, Beijing 100048, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0384

Abstract

Learning path recommendation is an important task in informatized education. Traditional data-driven methods usually act as black-box models. They lack interpretability and reduce learners’ trust. To address this issue, this study designed an interpretable and adaptive learning path recommendation framework. It leveraged the reasoning and generation abilities of a large language model (LLM) . This study constructed an LLM-based agent (LLM-Agent) . The agent consisted of a planner, an experiential knowledge base, a reflector, and an actor. The study adopted chain-of-thought prompting. The planner applied a hierarchical planning strategy. The reflector summarized each task, stored the generated experience in a vector database, and evaluated the experience value for effective reuse. The actor followed a two-step action strategy. It first performed semantic filtering to obtain a candidate set and then generated recommendations. This study conducted experiments in a simulated environment. It compared the proposed method with traditional approaches and other LLM-based methods. The study further investigated performance under different backbone LLMs. Results show that the proposed method outperforms baseline methods in effectiveness, content diversity, and accuracy. It also provides reliable explanations for recommended learning paths. The study demonstrates the reliability of the LLM-Agent and offers a new perspective for learning path recommendation methods.

Foundation Support

国家自然科学基金资助项目(62173007)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0384
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-15] Accepted Paper

Cite This Article

龚永罡, 莫鸿铭, 廉小亲, 等. 基于大语言模型的可解释性自适应学习路径推荐 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0384. (Gong Yonggang, Mo Hongming, Lian Xiaoqin, et al. Interpretable adaptive learning path recommendation based on large language model [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0384. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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