A Comprehensive Survey on Large-Language-Model-Based Agents for Education

Abstract

Modern education aims at providing students with more personalized learning services and more engaging learning experiences. One promising approach is to develop educational agents to facilitate high-quality completion of various educational tasks. In recent years, the advent of large language models (LLMs) has breathed new life into educational agents and pushed them into a new stage of intelligence. This survey tries to conduct a comprehensive and thorough investigation of LLM-based agents in education. First, the developments of educational agents are presented as background information. Subsequently, we propose a unified architecture for LLM-based educational agents, including perception, profiling, memory, reasoning, and action modules, and summarize two primary methods (i.e., fine-tuning and prompt engineering) for equipping them with abilities. Next, we categorize the potential applications of LLM-based educational agents across the “teaching–learning–assessment–research” chain, and discover that LLM-based educational agent can play significant roles in various educational tasks. Furthermore, we reveal that when assessing the effectiveness of LLM-based educational agents, subjective evaluation remains dominant, supplemented by objective evaluation. Finally, the open issues and future research directions in this field are discussed from multiple perspectives. We hope that this survey can provide valuable insights and inspirations for researchers and practitioners to enhance the further development of educational agents in the future.