EduAgent: Generative Student Agents in Learning
Songlin Xu, Xinyu Zhang, Lianhui Qin

TL;DR
EduAgent introduces a novel generative framework using large language models and cognitive prior knowledge to simulate diverse student behaviors in online education, outperforming existing models in realism and prediction accuracy.
Contribution
This work presents EduAgent, a new generative student agent framework that incorporates cognitive science knowledge to improve simulation of student behaviors using LLMs.
Findings
EduAgent accurately predicts real student behaviors.
It can generate realistic virtual student behaviors without real data.
Outperforms existing simulation models in educational contexts.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Text Readability and Simplification · Natural Language Processing Techniques
