# CoderAgent: Simulating Student Behavior for Personalized Programming Learning with Large Language Models

arXiv: https://arxiv.org/abs/2505.20642 · HTML: https://mlsanity.com/paper/2505.20642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 18 of 18)

- [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)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [EduAgent: Generative Student Agents in Learning](https://mlsanity.com/paper/2404.07963/) (2024)
- [Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling](https://mlsanity.com/paper/2403.14071/) (2024)
- [Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems](https://mlsanity.com/paper/2501.10332/) (2025)
- [Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents](https://mlsanity.com/paper/2311.13373/) (2023)
- [Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models](https://mlsanity.com/paper/2410.13343/) (2024)
- [LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System](https://mlsanity.com/paper/2501.15749/) (2025)
- [Programming Knowledge Tracing: A Comprehensive Dataset and A New Model](https://mlsanity.com/paper/2112.08273/) (2021)
- [E-QGen: Educational Lecture Abstract-based Question Generation System](https://mlsanity.com/paper/2404.13547/) (2024)
- [Social Learning through Interactions with Other Agents: A Survey](https://mlsanity.com/paper/2407.21713/) (2024)

## Built on by (most cited first, 4 of 4)

- [BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation](https://mlsanity.com/paper/2602.13280/) (2026)
- [ALIGNAgent: Adaptive Learner Intelligence for Gap Identification and Next-step guidance](https://mlsanity.com/paper/2601.15551/) (2026)
- [ToST: A Tree-of-Thought Socratic Teaching Framework for Multi-Path Guidance and Parallel Thinking](https://mlsanity.com/paper/2608.25775/) (2026)
- [From Learner Behavior to Reusable Skills for Effective and Efficient Learner Simulation](https://mlsanity.com/paper/2609.37157/) (2026)

## Abstract (arXiv)

Personalized programming tutoring, such as exercise recommendation, can enhance learners' efficiency, motivation, and outcomes, which is increasingly important in modern digital education. However, the lack of sufficient and high-quality programming data, combined with the mismatch between offline evaluation and real-world learning, hinders the practical deployment of such systems. To address this challenge, many approaches attempt to simulate learner practice data, yet they often overlook the fine-grained, iterative nature of programming learning, resulting in a lack of interpretability and granularity. To fill this gap, we propose a LLM-based agent, CoderAgent, to simulate students' programming processes in a fine-grained manner without relying on real data. Specifically, we equip each human learner with an intelligent agent, the core of which lies in capturing the cognitive states of the human programming practice process. Inspired by ACT-R, a cognitive architecture framework, we design the structure of CoderAgent to align with human cognitive architecture by focusing on the mastery of programming knowledge and the application of coding ability. Recognizing the inherent patterns in multi-layered cognitive reasoning, we introduce the Programming Tree of Thought (PTOT), which breaks down the process into four steps: why, how, where, and what. This approach enables a detailed analysis of iterative problem-solving strategies. Finally, experimental evaluations on real-world datasets demonstrate that CoderAgent provides interpretable insights into learning trajectories and achieves accurate simulations, paving the way for personalized programming education.