OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning

• URL: http://arxiv.org/abs/2509.14803v2
• Date: Sat, 20 Sep 2025 06:11:23 GMT
• Title: OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning
• Authors: Xian Gao, Zongyun Zhang, Ting Liu, Yuzhuo Fu,
• Abstract summary: We propose OnlineMate, a multi-agent learning companion system driven by large language models (LLMs)<n>OnlineMate is capable of simulating peer-like agent roles, adapting to learners' cognitive states during collaborative discussions, and inferring their psychological states, such as misunderstandings, confusion, or motivation.<n> Experimental results in simulated learning scenarios demonstrate that OnlineMate effectively fosters deep learning and discussions while enhancing cognitive engagement in online educational settings.
• Score: 20.08144763551689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In online learning environments, students often lack personalized peer interactions, which play a crucial role in supporting cognitive development and learning engagement. Although previous studies have utilized large language models (LLMs) to simulate interactive dynamic learning environments for students, these interactions remain limited to conversational exchanges, lacking insights and adaptations to the learners' individualized learning and cognitive states. As a result, students' interest in discussions with AI learning companions is low, and they struggle to gain inspiration from such interactions. To address this challenge, we propose OnlineMate, a multi-agent learning companion system driven by LLMs that integrates the Theory of Mind (ToM). OnlineMate is capable of simulating peer-like agent roles, adapting to learners' cognitive states during collaborative discussions, and inferring their psychological states, such as misunderstandings, confusion, or motivation. By incorporating Theory of Mind capabilities, the system can dynamically adjust its interaction strategies to support the development of higher-order thinking and cognition. Experimental results in simulated learning scenarios demonstrate that OnlineMate effectively fosters deep learning and discussions while enhancing cognitive engagement in online educational settings.

Related papers

• AgentTutor: Empowering Personalized Learning with Multi-Turn Interactive Teaching in Intelligent Education Systems [11.202091624300062]
  AgentTutor is a multi-turn interactive intelligent education system to empower personalized learning.<n>It features an LLM-powered generative multi-agent system and a learner-specific personalized learning profile environment.<n>It includes five key modules: curriculum decomposition, learner assessment, dynamic strategy, teaching reflection, and knowledge & experience memory.
  arXiv  Detail & Related papers  (2025-12-24T12:26:28Z)
• Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis [0.15293427903448018]
  This paper presents a psychologically-aware conversational agent designed to enhance both learning performance and emotional well-being in educational settings.<n>The system combines Large Language Models (LLMs), a knowledge graph-enhanced BERT (KG-BERT), and a bidirectional Long Short-Term Memory (LSTM) with attention to classify students' cognitive and affective states in real time.
  arXiv  Detail & Related papers  (2025-12-11T09:06:45Z)
• UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models [59.693733170193944]
  Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings.<n>Recent reinforcement learning approaches address this limitation but face two critical challenges.<n>We propose the Unidirectional Cognitive Optimization (UCO) method to address these challenges.
  arXiv  Detail & Related papers  (2025-11-12T01:27:02Z)
• The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation [55.722188569369656]
  This paper introduces a novel framework for memory-based student simulation.<n>It incorporates developmental trajectories through a hierarchical memory mechanism with structured knowledge representation.<n>In practice, we implement a curriculum-aligned simulator grounded on the Next Generation Science Standards.
  arXiv  Detail & Related papers  (2025-11-08T08:05:43Z)
• Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties [3.1146704506932985]
  Large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior.<n>We present a multi-agent LLM simulation framework in which agents repeatedly interact, evaluate one another, and adapt their behavior through in-context learning.
  arXiv  Detail & Related papers  (2025-10-22T07:00:33Z)
• AI Pedagogy: Dialogic Social Learning for Artificial Agents [0.6553587309274792]
  This study explores the potential of socially mediated learning paradigms to address limitations of traditional AI training approaches.<n>We introduce a dynamic environment, termed the 'AI Social Gym', where an AI learner agent engages in dyadic pedagogical dialogues with knowledgeable AI teacher agents.<n>Our investigation focuses on how different pedagogical strategies impact the AI learning process in the context of acquisition.
  arXiv  Detail & Related papers  (2025-05-25T11:19:48Z)
• Examining the Role of LLM-Driven Interactions on Attention and Cognitive Engagement in Virtual Classrooms [9.241265477406078]
  We investigate how peer question-asking behaviors influenced student engagement, attention, cognitive load, and learning outcomes.<n>Our results suggest that peer questions did not introduce extraneous cognitive load directly, as the cognitive load is strongly correlated with increased attention to the learning material.
  arXiv  Detail & Related papers  (2025-05-12T09:21:19Z)
• Playpen: An Environment for Exploring Learning Through Conversational Interaction [81.67330926729015]
  We investigate whether Dialogue Games can also serve as a source of feedback signals for learning.<n>We introduce Playpen, an environment for off- and online learning through Dialogue Game self-play.<n>We find that imitation learning through SFT improves performance on unseen instances, but negatively impacts other skills.
  arXiv  Detail & Related papers  (2025-04-11T14:49:33Z)
• Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning [42.09560737219404]
  Large Language Models show promise in human-like communication, but their standalone use is hindered by memory constraints and contextual incoherence.<n>This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction.<n>To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences.
  arXiv  Detail & Related papers  (2025-04-02T10:45:41Z)
• The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course [1.9116784879310025]
  Large language models (LLMs) have significantly impacted education, raising both opportunities and challenges.<n>We introduce StudyChat, a dataset capturing real-world student interactions with an LLM.<n>We deploy a web application that replicates ChatGPTs core functionalities, and use it to log student interactions with the LLM.<n>We analyze these interactions, highlight usage trends, and analyze how specific student behavior correlates with their course outcome.
  arXiv  Detail & Related papers  (2025-03-11T00:17:07Z)
• Multi-agent cooperation through learning-aware policy gradients [53.63948041506278]
  Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning.<n>We present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning.<n>We derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
  arXiv  Detail & Related papers  (2024-10-24T10:48:42Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation [54.97931304488993]
  Self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. We propose an online learning method, SELFI, that leverages online robot experience to rapidly fine-tune pre-trained control policies. We report improvements in terms of collision avoidance, as well as more socially compliant behavior, measured by a human user study.
  arXiv  Detail & Related papers  (2024-03-01T21:27:03Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.