The field of education is witnessing significant advancements with the integration of Artificial Intelligence (AI) and Large Language Models (LLMs). Recent studies have explored the potential of AI in enhancing math learning, assessing student performance, and providing personalized feedback. The use of LLMs has shown promise in evaluating student exams, generating feedback, and addressing student appeals. Additionally, AI-driven platforms are being developed to support students' reading and cognition, and to facilitate material-grounded asynchronous discussions in flipped learning environments.

Noteworthy papers in this area include:

• Integrating LLMs for Grading and Appeal Resolution in Computer Science Education, which introduces AI-PAT, an AI-powered assessment tool that leverages LLMs to evaluate computer science exams and handle student appeals.
• EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework, which presents a multi-agent dialogue framework for assessing teaching capabilities of LLMs through simulated dynamic educational scenarios.