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] |
| Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 192828633 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: How to design LLM-based intelligent agent to support student’s learning in educational context: A systematic literature review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cai%2C+Huiying%22">Cai, Huiying</searchLink><relatesTo>1</relatesTo><i> caihy@jiangnan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Bing%22">Han, Bing</searchLink><relatesTo>1</relatesTo><i> 6232006005@stu.jiangnan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Jiayue%22">Sun, Jiayue</searchLink><relatesTo>1</relatesTo><i> 6242006010@stu.jiangnan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Yu%22">Cheng, Yu</searchLink><relatesTo>1</relatesTo><i> 1158220208@stu.jiangnan.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Technology+%26+Society%22">Educational Technology & Society</searchLink>. Apr2026, Vol. 29 Issue 2, p249-269. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Interactive+learning%22">Interactive learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems+design%22">Instructional systems design</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=192828633 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.30191/ETS.202604_29(2).SP03 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 249 Subjects: – SubjectFull: Educational technology Type: general – SubjectFull: Interactive learning Type: general – SubjectFull: Individualized instruction Type: general – SubjectFull: Intelligent tutoring systems Type: general – SubjectFull: Instructional systems design Type: general – SubjectFull: Instructional systems Type: general – SubjectFull: Software architecture Type: general Titles: – TitleFull: How to design LLM-based intelligent agent to support student’s learning in educational context: A systematic literature review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cai, Huiying – PersonEntity: Name: NameFull: Han, Bing – PersonEntity: Name: NameFull: Sun, Jiayue – PersonEntity: Name: NameFull: Cheng, Yu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11763647 Numbering: – Type: volume Value: 29 – Type: issue Value: 2 Titles: – TitleFull: Educational Technology & Society Type: main |
| ResultId | 1 |
