LLM Agents for Education: Advances and Applications (2025)

Abstract: Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes. In this survey, we present a systematic review of recent advances in applying LLM agents to address key challenges in educational settings, such as feedback comment generation, curriculum design, etc. We analyze the technologies enabling these agents, including representative datasets, benchmarks, and algorithmic frameworks. Additionally, we highlight key challenges in deploying LLM agents in educational settings, including ethical issues, hallucination and overreliance, and integration with existing educational ecosystems. Beyond the core technical focus, we include in Appendix A a comprehensive overview of domain-specific educational agents, covering areas such as science learning, language learning, and professional development.

## introduction artificial intelligence techniques are increasingly used in education to enable personalized learning and intelligent tutoring @xcite @xcite . while traditional educational data mining approaches @xcite @xcite @xcite , such as knowledge tracing and cognitive diagnosis, have made significant progress in reshaping the humanlearning paradigm by analyzing student behaviors and assessing knowledge states, they still face major challenges in real-world applications. these challenges include shallow contextual understanding, limited interactive capabilities, and difficulties in generating adaptive, personalized learning materials, etc @xcite @xcite ). the strong natural language understanding of large language models (llms) and the task automation capabilities of llm agents make them valuable for addressing challenges in education @xcite . first, memory enables llm agents to retain both long-term knowledge about students' study habits and shortterm context from real-time interactions, enhancing contextual understanding and ensuring personalized learning experiences across various educational tasks @xcite . second, tool use allows llm agents to access external resources, perform complex calculations, and retrieve realtime information, enabling them to automate intricate educational tasks such as grading, knowledge retrieval, and adaptive content generation, thereby overcoming limited interactivity and enhancing engagement @xcite . third, planning supports structured learning by decomposing complex topics, predicting optimal learning paths, and dynamically adjusting instructional strategies, allowing llm agents to autonomously guide students through personalized learning experiences @xcite . in addition to these core architectural capabilities, we identify personalization, explainability, and multi-agent communication as essential for effective educational llm agents, as they enable nuanced instructional behavior and collaborative reasoning. personalization allows agents to tailor instruction to individual learners' needs and preferences @xcite . explainability enables them to provide interpretable justifications for feedback and decisions @xcite . multi-agent communication facilitates role-based collaboration, such as between a planner and a critic, to improve robustness and task coverage @xcite . by integrating these features, llm agents enhance understanding and engagement while streamlining educational workflows for greater adaptability and efficiency. in this survey, we provide a comprehensive review of llm agents in educational settings, with a focus on their underlying technical foundations and general-purpose pedagogical capabilities. we begin by introducing the core abilities of educational llm agents, including memory, tool use, planning, personalization, explainability, and multiagent communication, and discuss their potential to automate and enhance diverse educational tasks. given the highly applied nature of the education domain, we propose a task-centric taxonomy in figure 1 that organizes recent advances around core educational tasks. we categorize educational llm agents based on their roles in supporting teachers and students, capturing both instructional and learning-focused functions: (1) teaching assistance agents, which support educators by automating tasks such as classroom simulation (cs), feedback comment generation (fcg), and curriculum design (cd); and (2) student support agents, which facilitate personalized learning through adaptive learning (al), knowledge tracing (kt), and error correction and detection (ecd). the overview of llm agents for education and illustrative examples of each task are presented in figure 2 . we further highlight critical challenges in deploying these agents, including ethical issues, hallucination and overreliance, and integration into existing educational ecosystems. finally, we compile essential datasets, benchmarks, and evaluation protocols to support future research on llm agentbased educational systems. we summarize our contributions as follows: ## llm agents for education llm agents have a set of connected abilities that help them handle complex tasks and provide meaningful support in education. these core features work together to let llm agents do more than just find information-they can interact in ways that are flexible, responsive, and tailored to each learner. their main strengths in education include strong memory, the ability to use tools, planning skills, personalization, clear explanations, and the ability to work with other agents. specifically, memory. memory in llm agents includes longterm (foundational knowledge, e.g., commonsense) and short-term (current interaction data) components @xcite . active management via summarization and retrieval ensures relevant context is maintained @xcite @xcite . this allows agents to track student progress and personalize responses, though sophisticated filtering is crucial to maintain the quality against noisy interaction data. tool use. to overcome limitations like knowledge cutoffs or calculation difficulties, llm agents use external tools such as search engines, databases, or apis @xcite . this expands their functionality, providing access to current information and specialized capabilities. by integrating these tools, llm agents expand their functional capabilities, ensuring that they can provide accurate and relevant information while supporting diverse educational tasks. planning. planning allows agents to actively support learning by breaking down complex goals into smaller, manageable steps and adjusting based on how the student is doing @xcite . this means understanding the learning goals, creating step-by-step plans, personalizing the learning path, and making changes based on feedback and progress. for longer-term tasks, llms can act as "metacontrollers," using methods like "pedagogical steering" to stay aligned with teaching goals, which thus leads to dynamic, emergent curricula co-created with students @xcite . personalization. personalization is a hallmark of effective education, and llm agents excel in this area by adapting to individual learning styles and needs @xcite . they can serve as tutors, teaching assistants, or even simulate peer interactions to enhance the learning experience @xcite . additionally, llm agents can simulate user behaviors in recommendation systems, which can be applied to personalize educational content @xcite . such a technology of personalization transforms traditional education, making it more accessible and tailored to each student's unique needs. ## agent for teaching assistance agents for teaching assistance are designed to support educators in the learning environment. these agents leverage llms to provide personalized, scalable, and efficient support across various aspects of the educational process. their primary objectives are to enhance teaching quality, enrich student learning experiences, and reduce educators' workload. by incorporating advanced capabilities, llm agents can effectively support key educational tasks, including classroom simulation ( §3.1), feedback comment generation ( §3.2), and curriculum design ( §3.3). ## agent for student support llm agent-based student support systems aim to provide real-time personalized assistance without requiring direct teacher involvement. unlike traditional rule-based systems, these agents offer inter-active and adaptive feedback, enabling students to progress at their own pace. core educational tasks in student support include adaptive learning ( §4.1), knowledge tracing ( §4.2), and error correction and detection ( §4.3). ## challenges and future directions in this section, we discuss key challenges that must be addressed to ensure the effective, reliable, and ethical deployment of llm agents in educational settings. we focus on three critical areas: (1) privacy, bias, and fairness; (2) hallucination and overreliance; and (3) integration with existing educa-tional ecosystems. for each of these challenges, we outline potential research directions aimed at improving the robustness, trustworthiness, and practical applicability of llm agents in real-world education environments. ## conclusion in this survey, we presented a comprehensive review of llm agents for education, focusing on their technical foundations and their potential to transform personalized learning, intelligent tutoring, and pedagogical automation. we proposed a task-centric taxonomy that categorizes llm agents into teaching assistance and student support, highlighting their core capabilities such as memory augmentation, tool use, planning, and personalization. we also examined key research challenges, including ethical issues, hallucination and overreliance, and integration with existing educational ecosystems, which must be addressed to ensure reliable and ethical deployment. to support continued progress in this field, we compiled critical datasets, benchmarks, and evaluation methodologies. as llm agents continue to evolve, their influence on education will expand. realizing their full potential, however, will require both technical rigor and thoughtful system design. we hope this survey provides a solid foundation for future research and the development of ai-powered educational systems.