LLM Agents for Education: Advances and Applications

2025-03-14

AI Summary

This paper addresses the practical deployment challenges of large language model (LLM)-based agents in educational settings. To this end, it proposes the first education-oriented dual-track taxonomy—distinguishing *instructional agents* (focused on pedagogical interaction) from *domain-specific agents* (targeting subject-matter expertise)—and systematically synthesizes key technical advances: education-aware fine-tuning, multi-turn pedagogical dialogue modeling, knowledge-augmented reasoning, explainability alignment, and educational API integration. It constructs a comprehensive technology landscape spanning the entire educational pipeline, unifying education-specific datasets, evaluation benchmarks, and algorithmic paradigms. Furthermore, it critically analyzes four pressing real-world challenges: privacy preservation, bias and fairness mitigation, hallucination reduction, and ecosystem interoperability. The work delivers a unified reference framework for researchers and practitioners, advancing the development of trustworthy, equitable, and production-ready educational agents.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications. In this survey, we provide a systematic review of state-of-the-art research on LLM agents in education, categorizing them into two broad classes: (1) emph{Pedagogical Agents}, which focus on automating complex pedagogical tasks to support both teachers and students; and (2) emph{Domain-Specific Educational Agents}, which are tailored for specialized fields such as science education, language learning, and professional development. We comprehensively examine the technological advancements underlying these LLM agents, including key datasets, benchmarks, and algorithmic frameworks that drive their effectiveness. Furthermore, we discuss critical challenges such as privacy, bias and fairness concerns, hallucination mitigation, and integration with existing educational ecosystems. This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.

Problem

• Systematic review of LLM agents in education
• Categorization into Pedagogical and Domain-Specific Agents
• Addressing challenges like privacy, bias, and integration

Innovation

• Automates complex pedagogical tasks effectively
• Tailors agents for specialized educational fields
• Addresses privacy, bias, and integration challenges