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

Xinyu Jessica Wang, Christine Lee, Bilge Mutlu

TL;DR

The paper tackles the challenge of personalizing online learning by proposing four guidelines—goals, time, pace, and path—for LLM-driven learning plan generation and by implementing LearnMate, an LLM-based system that creates personalized plans and offers real-time, context-aware support. LearnMate employs a dual-LLM architecture (strategic planning and calendar visualization) and a calendar-based interface, using an internal syllabus proxy and YouTube transcripts to deliver structured, actionable learning experiences. Compared with a single-agent LLM, LearnMate demonstrates improved adaptivity, dynamic pacing, visual interactivity, and context-aligned assistance, illustrating the value of a structured personalization framework in online education. The work discusses practical implications, limitations, and future directions, including broader platform integration and enhanced learning analytics, to advance accessible, adaptive online learning at scale.

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.

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

TL;DR

The paper tackles the challenge of personalizing online learning by proposing four guidelines—goals, time, pace, and path—for LLM-driven learning plan generation and by implementing LearnMate, an LLM-based system that creates personalized plans and offers real-time, context-aware support. LearnMate employs a dual-LLM architecture (strategic planning and calendar visualization) and a calendar-based interface, using an internal syllabus proxy and YouTube transcripts to deliver structured, actionable learning experiences. Compared with a single-agent LLM, LearnMate demonstrates improved adaptivity, dynamic pacing, visual interactivity, and context-aligned assistance, illustrating the value of a structured personalization framework in online education. The work discusses practical implications, limitations, and future directions, including broader platform integration and enhanced learning analytics, to advance accessible, adaptive online learning at scale.

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.

Paper Structure

This paper contains 25 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Design of Personalization Guidelines for LLMs
  4. Personalization Dimensions
  5. User Interaction with LearnMate
  6. Guiding User Scenario
  7. Course Selection
  8. How it Works
  9. Personalized Learning Plan Generation
  10. How it Works
  11. Real-time Learning Support
  12. How it Works
  13. Comparison of LearnMate to Single-agent LLM
  14. Personalized Learning Framework and Adaptivity
  15. Visualization and User Interactivity
  16. ...and 10 more sections

Figures (3)

  • Figure 1: The LearnMatecourse selection interface. Users begin by defining their learning goals through either: (1) specifying learning interests (step ) to generate course recommendations (step ), or (2) browsing courses by topic (step ). We outline the user’s interaction with this interface as a guide to explain the pipeline of LearnMate in Section \ref{['goalGuidelines']}.
  • Figure 2: The LearnMatepersonalized planning interface. Users define the remaining dimensions (time, pace, and path) (step ) in order to generate a learning plan (step ) and interactive calendar (step ). We outline the user’s interaction with this interface as a guide to explain the pipeline of LearnMate in Section \ref{['personalizationGuidelines']}.
  • Figure 3: The LearnMatereal-time learning support interface. Users can ask questions during their learning process and receive immediate, context-specific guidance (step ). We outline the user’s interaction with this interface as a guide to explain the pipeline of LearnMate in Section \ref{['RealtimelearningSupport']}.