# EduAgent: Generative Student Agents in Learning

arXiv: https://arxiv.org/abs/2404.07963 · HTML: https://mlsanity.com/paper/2404.07963/

## Key facts

- Cited by 42 papers in the map (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 24 of its 82 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 24 of 24)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://mlsanity.com/paper/2305.17390/) (2023)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [On Generative Agents in Recommendation](https://mlsanity.com/paper/2310.10108/) (2023)
- [MindAgent: Emergent Gaming Interaction](https://mlsanity.com/paper/2309.09971/) (2023)
- [CGMI: Configurable General Multi-Agent Interaction Framework](https://mlsanity.com/paper/2308.12503/) (2023)
- [Prediction and Localization of Student Engagement in the Wild](https://mlsanity.com/paper/1804.00858/) (2018)
- [Cognitive Model Priors for Predicting Human Decisions](https://mlsanity.com/paper/1905.09397/) (2019)
- [Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing](https://mlsanity.com/paper/1809.08713/) (2018)
- [A large language model-assisted education tool to provide feedback on open-ended responses](https://mlsanity.com/paper/2308.02439/) (2023)
- [Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2104.04034/) (2021)
- [Leveraging generative artificial intelligence to simulate student learning behavior](https://mlsanity.com/paper/2310.19206/) (2023)
- [Lending Interaction Wings to Recommender Systems with Conversational Agents](https://mlsanity.com/paper/2310.04230/) (2023)
- [The Use of Multiple Conversational Agent Interlocutors in Learning](https://mlsanity.com/paper/2312.16534/) (2023)
- [Peer attention enhances student learning](https://mlsanity.com/paper/2312.02358/) (2023)

## Built on by (most cited first, 30 of 42)

- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education](https://mlsanity.com/paper/2404.06711/) (2024)
- [Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems](https://mlsanity.com/paper/2501.10332/) (2025)
- [TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students](https://mlsanity.com/paper/2410.04078/) (2024)
- [LLM-Powered AI Agent Systems and Their Applications in Industry](https://mlsanity.com/paper/2505.16120/) (2025)
- [EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design](https://mlsanity.com/paper/2504.05370/) (2025)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [RTBAgent: A LLM-based Agent System for Real-Time Bidding](https://mlsanity.com/paper/2502.00792/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [Knowledge is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis](https://mlsanity.com/paper/2502.05556/) (2025)
- [Towards Robust Evaluation of STEM Education: Leveraging MLLMs in Project-Based Learning](https://mlsanity.com/paper/2505.17050/) (2025)
- [FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets](https://mlsanity.com/paper/2508.11401/) (2025)
- [From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization](https://mlsanity.com/paper/2505.16832/) (2025)
- [LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systems](https://mlsanity.com/paper/2512.02321/) (2025)
- [CoderAgent: Simulating Student Behavior for Personalized Programming Learning with Large Language Models](https://mlsanity.com/paper/2505.20642/) (2025)
- [Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation](https://mlsanity.com/paper/2602.11790/) (2026)
- [Mix-Ecom: Towards Mixed-Type E-Commerce Dialogues with Complex Domain Rules](https://mlsanity.com/paper/2509.23836/) (2025)
- [Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs](https://mlsanity.com/paper/2511.15163/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [AgentTutor: Empowering Personalized Learning with Multi-Turn Interactive Teaching in Intelligent Education Systems](https://mlsanity.com/paper/2601.04219/) (2025)
- [Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call](https://mlsanity.com/paper/2606.15225/) (2026)
- [From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes](https://mlsanity.com/paper/2503.06525/) (2025)
- [InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation](https://mlsanity.com/paper/2508.03174/) (2025)
- [IB-GRPO: Aligning LLM-based Learning Path Recommendation with Educational Objectives via Indicator-Based Group Relative Policy Optimization](https://mlsanity.com/paper/2601.14686/) (2026)
- [GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions](https://mlsanity.com/paper/2601.20402/) (2026)
- [BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation](https://mlsanity.com/paper/2602.13280/) (2026)
- [A Scoping Review of Large Language Model-Based Pedagogical Agents](https://mlsanity.com/paper/2604.12253/) (2026)
- [AgentSME for Simulating Diverse Communication Modes in Smart Education](https://mlsanity.com/paper/2508.03109/) (2025)

## Abstract (arXiv)

Student simulation in online education is important to address dynamic learning behaviors of students with diverse backgrounds. Existing simulation models based on deep learning usually need massive training data, lacking prior knowledge in educational contexts. Large language models (LLMs) may contain such prior knowledge since they are pre-trained from a large corpus. However, because student behaviors are dynamic and multifaceted with individual differences, directly prompting LLMs is not robust nor accurate enough to capture fine-grained interactions among diverse student personas, learning behaviors, and learning outcomes. This work tackles this problem by presenting a newly annotated fine-grained large-scale dataset and proposing EduAgent, a novel generative agent framework incorporating cognitive prior knowledge (i.e., theoretical findings revealed in cognitive science) to guide LLMs to first reason correlations among various behaviors and then make simulations. Our two experiments show that EduAgent could not only mimic and predict learning behaviors of real students but also generate realistic learning behaviors of virtual students without real data.