How to design LLM-based intelligent agent to support student’s learning in educational context: A systematic literature review.
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| Title: | How to design LLM-based intelligent agent to support student’s learning in educational context: A systematic literature review. |
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| Authors: | Cai, Huiying1 caihy@jiangnan.edu.cn, Han, Bing1 6232006005@stu.jiangnan.edu.cn, Sun, Jiayue1 6242006010@stu.jiangnan.edu.cn, Cheng, Yu1 1158220208@stu.jiangnan.edu.cn |
| Source: | Educational Technology & Society. Apr2026, Vol. 29 Issue 2, p249-269. 21p. |
| Subject Terms: | *Educational technology, *Interactive learning, *Individualized instruction, *Intelligent tutoring systems, *Instructional systems design, *Instructional systems, Software architecture |
| Abstract: | Large language model (LLM)-based intelligent agents are increasingly integrated into educational settings to support personalized, adaptive, and interactive learning. Despite growing research, it remains unclear how to effectively design LLM-based intelligent agents to support learning in educational contexts. This systematic review analyzed 40 empirical studies published between January 2020 and April 2025 on the design and application of LLM-based agents in education following PRISMA procedures. To address the research questions, we employed a structured coding framework to analyze the selected literature. Specifically, the technological architectures were analyzed according to four components—profile, memory, planning, and action, while the pedagogical integration was examined through thematic analysis across five dimensions—deployment mode, interaction mode, role assignment, function, and response forms. Based on these analyses, we identify principles for designing technological architectures and integrating agents pedagogically, offering actionable insights for practitioners. Findings further highlight the importance of interdisciplinary co-design between AI developers and learning scientists, ethical data preparation, and grounding in learning sciences to guide future research and development of LLM-based agents that provide adaptive, equitable, and context-sensitive learning support. [ABSTRACT FROM AUTHOR] |
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| Database: | Education Research Complete |
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