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AI Agents in Education: Four Trends and a Practical Workflow

Jiali Zhang

2025 · DOI: 10.1609/aaaiss.v5i1.35578
AAAI Spring Symposia · 1 Citations

TLDR

A common AI agent prototype is introduced, focusing on customization through fine-tuning and retrieval-augmented generation (RAG), and the role of external tools in enhancing versatility is highlighted.

Abstract

AI agents based on large language models (LLMs) are transforming education by offering personalized tutoring, faster grading, innovative content generation, and streamlined administrative tasks. This paper explores four major trends of AI agents in the education industry: tutoring and mentoring, automated grading, content creation, and administrative automation. This study also introduces a common AI agent prototype, focusing on customization through fine-tuning and retrieval-augmented generation (RAG), and highlights the role of external tools in enhancing versatility. By reviewing current applications and technical methods, this work demonstrates the growing role of AI in improving instructional quality and operational efficiency.