mlsanityData to 14 Oct 2026

Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models

arXiv 2508.17310 ↗First posted 24 Aug 2025cs.CL cs.CYcited by 0

On the map: LLM agents and tool use → Educational agents and math problem solving

Abstract (arXiv)

Interactive online learning environments, represented by Massive AI-empowered Courses (MAIC), leverage LLM-driven multi-agent systems to transform passive MOOCs into dynamic, text-based platforms, enhancing interactivity through LLMs. This paper conducts an empirical study on a specific MAIC course to explore three research questions about dropouts in these interactive online courses: (1) What factors might lead to dropouts? (2) Can we predict dropouts? (3) Can we reduce dropouts? We analyze interaction logs to define dropouts and identify contributing factors. Our findings reveal strong links between dropout behaviors and textual interaction patterns. We then propose a course-progress-adaptive dropout prediction framework (CPADP) to predict dropouts with at most 95.4% accuracy. Based on this, we design a personalized email recall agent to re-engage at-risk students. Applied in the deployed MAIC system with over 3,000 students, the feasibility and effectiveness of our approach have been validated on students with diverse backgrounds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 4 of its 25 references that are in the map. First posted on arXiv 24 Aug 2025.

Builds on

4 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents5 Sep
  2. 2024Simulating Classroom Education with LLM-Empowered Agents27 Jun

Other references · 2

  1. 2024Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUTcited by 14
  2. 2024Artificial Human Lecturers: Initial Findings From Asia's First AI Lecturers in Class to Promote Innovation in Educationcited by 1

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Similar, not cited

Close in wording, no citation either way

  1. 2025Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion
  2. 2025EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework
  3. 2025LLM Agents for Education: Advances and Applications
  4. 2025Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment
  5. 2026A Scoping Review of Large Language Model-Based Pedagogical Agents
  6. 2025Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
  7. 2026Large Language Models in Teaching and Learning: Reflections on Implementing an AI Chatbot in Higher Education ↗
  8. 2025OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning