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Defining and Classifying the Roles of Intelligent Learning Companion Systems: A Scoping Review of the Literature.

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Title: Defining and Classifying the Roles of Intelligent Learning Companion Systems: A Scoping Review of the Literature.
Authors: Han, Yujie1 (AUTHOR) jadey.han@snu.ac.kr, Hong, Sumin1 (AUTHOR) hongxiumin@snu.ac.kr, Li, Zhenyan1 (AUTHOR) 2020_35525@snu.ac.kr, Lim, Cheolil1 (AUTHOR) chlim@snu.ac.kr
Source: TechTrends: Linking Research & Practice to Improve Learning. May2025, Vol. 69 Issue 3, p567-581. 15p.
Subject Terms: *Artificial intelligence, *Learning, *Educational outcomes, *Instructional systems, *Individualized instruction, Adaptive control systems, Similarity (Psychology)
Abstract: This scoping review investigates the roles of intelligent learning companion systems (LCS) within educational settings, as well as the presences artificial intelligence (AI) embodies within these roles, and their application in education. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we analyzed 24 studies on the multifaceted nature of LCS roles and their implications for learning. AI-empowered LCS were identified as adaptive systems that emulate human learning companions, providing a dynamic social context to offer learners support and companionship. By simulating human-like characteristics, AI-empowered companions enhance personalized learning experiences and outcomes, optimizing educational outcomes. Our review categorized 14 roles played by intelligent LCS, underlining their versatility and potential to adapt to and meet learners' needs and abilities. This review offers a comprehensive classification of LCS roles, elucidating their presences and illustrating their practical applications in educational contexts. The findings highlight the transformative role of AI in education, demonstrating its capability to mimic and augment human teaching and learning interactions. [ABSTRACT FROM AUTHOR]
Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature 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
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  Data: This scoping review investigates the roles of intelligent learning companion systems (LCS) within educational settings, as well as the presences artificial intelligence (AI) embodies within these roles, and their application in education. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we analyzed 24 studies on the multifaceted nature of LCS roles and their implications for learning. AI-empowered LCS were identified as adaptive systems that emulate human learning companions, providing a dynamic social context to offer learners support and companionship. By simulating human-like characteristics, AI-empowered companions enhance personalized learning experiences and outcomes, optimizing educational outcomes. Our review categorized 14 roles played by intelligent LCS, underlining their versatility and potential to adapt to and meet learners' needs and abilities. This review offers a comprehensive classification of LCS roles, elucidating their presences and illustrating their practical applications in educational contexts. The findings highlight the transformative role of AI in education, demonstrating its capability to mimic and augment human teaching and learning interactions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature 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.)
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              Text: May2025
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