关键词: 数据合成, 大语言模型, 检索增强生成, 智能体, 人类行为模拟, 冷启动, 智能教育

Abstract: Personalized learning has become a core focus of the digital transformation in education,with its success hinging on the precise understanding and modeling of student response data.However,real-world educational scenarios often face challenges such as sparse learning behaviors,dynamic evolution of learning states,and privacy compliance constraints.Additionally,discrepancies between offline data and online learning behaviors result in insufficient behavioral data and distribution shifts,significantly limiting the modeling capabilities and generalization performance of intelligent education systems.To alleviate these dilemmas,previous studies have attempted to simulate student response behaviors to expand data scale and improve model performance. However,existing methods struggle to simultaneously balance generation quality,efficiency,and resource costs.To overcome these limitations,this paper proposes Agent4Stu,a student response behavior simulation framework that integrates large language models(LLMs) with retrieval-augmented generation(RAG) techniques,enabling low-cost,efficient,and highly generalizable personalized response generation.The framework comprises a pre-built retrieval database with retrieval strategies and an LLM-based agent.The retrieval database is constructed from student response behaviors,and two retrieval strategies are designed:similar-student collaborative retrieval and relevant-fact retrieval.These are combined with each student’s short-term memory to dynamically generate highly relevant prompts.Internally,the agent integrates three core modules,profile,memory,and action,which are responsible for modeling students’ learning characteristics and cognitive abilities,integrating historical experiences with know-ledge from the retrieval database,and simulating students’ responses on specific items based on their profile,memory,and know-ledge mastery.Compared with existing LLM-based student agents,Agent4Stu has a smaller memory footprint and simplified action reasoning,while leveraging a behavior-oriented structured retrieval database to provide auxiliary information.This design enables low-cost,efficient,and highly generalizable personalized response generation.Quantitative and qualitative experiments on two real-world learning datasets demonstrate the effectiveness and superiority of Agent4Stu in simulating student learning response behaviors.

Key words: Data synthesis, Large language models, Retrieval-augmented generation, Agents, Human behavior simulation, Cold-start, Intelligent education