Authors: Weibo Gao, Qi Liu, Linan Yue, Fangzhou Yao, Rui Lv, Zheng Zhang, Hao Wang, Zhenya Huang
Abstract: Personalized learning represents a promising educational strategy within
intelligent educational systems, aiming to enhance learners’ practice
efficiency. However, the discrepancy between offline metrics and online
performance significantly impedes their progress. To address this challenge, we
introduce Agent4Edu, a novel personalized learning simulator leveraging recent
advancements in human intelligence through large language models (LLMs).
Agent4Edu features LLM-powered generative agents equipped with learner profile,
memory, and action modules tailored to personalized learning algorithms. The
learner profiles are initialized using real-world response data, capturing
practice styles and cognitive factors. Inspired by human psychology theory, the
memory module records practice facts and high-level summaries, integrating
reflection mechanisms. The action module supports various behaviors, including
exercise understanding, analysis, and response generation. Each agent can
interact with personalized learning algorithms, such as computerized adaptive
testing, enabling a multifaceted evaluation and enhancement of customized
services. Through a comprehensive assessment, we explore the strengths and
weaknesses of Agent4Edu, emphasizing the consistency and discrepancies in
responses between agents and human learners. The code, data, and appendix are
publicly available at https://github.com/bigdata-ustc/Agent4Edu.
Source: http://arxiv.org/abs/2501.10332v1
