LearnMate: Enhancing Online Education with LLM-Powered Personalized Learning Plans and Support

With the increasing prevalence of online learning, adapting education to diverse learner needs remains a persistent challenge. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), promise powerful tools and capabilities to enhance personalized learning in online educational environments. In this work, we explore how LLMs can improve personalized learning experiences by catering to individual user needs toward enhancing the overall quality of online education. We designed personalization guidelines based on the growing literature on personalized learning to ground LLMs in generating tailored learning plans. To operationalize these guidelines, we implemented LearnMate, an LLM-based system that generates personalized learning plans and provides users with real-time learning support. We discuss the implications and future directions of this work, aiming to move beyond the traditional one-size-fits-all approach by integrating LLM-based personalized support into online learning environments.

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Publication Details

Published
2025-04-23
DOI
https://doi.org/10.1145/3706599.3719857
Citations
12
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
27.29
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article

LearnMate: Enhancing Online Education with LLM-Powered Personalized Learning Plans and Support

Bilge Mutlu, X. Wang, Christine P. Lee
12 citations
Intelligent Tutoring Systems and Adaptive Learning
27.29
article

LearnMate: Enhancing Online Education with LLM-Powered Personalized Learning Plans and Support

Bilge Mutlu, X. Wang, Christine P. Lee
article en
12 citations

Abstract

With the increasing prevalence of online learning, adapting education to diverse learner needs remains a persistent challenge. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), promise powerful tools and capabilities to enhance personalized learning in online educational environments. In this work, we explore how LLMs can improve personalized learning experiences by catering to individual user needs toward enhancing the overall quality of online education. We designed personalization guidelines based on the growing literature on personalized learning to ground LLMs in generating tailored learning plans. To operationalize these guidelines, we implemented LearnMate, an LLM-based system that generates personalized learning plans and provides users with real-time learning support. We discuss the implications and future directions of this work, aiming to move beyond the traditional one-size-fits-all approach by integrating LLM-based personalized support into online learning environments.

University of Wisconsin–Madison (US)
Quality Education
Openalex Percentile: Top 1%
Intelligent Tutoring Systems and Adaptive Learning
27.29
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