This perspective paper proposes a series of interactive scenarios that
utilize Artificial Intelligence (AI) to enhance classroom teaching, such as
dialogue auto-completion, knowledge and style transfer, and assessment of
AI-generated content. By leveraging recent developments in Large Language
Models (LLMs), we explore the potential of AI to augment and enrich
teacher-student dialogues and improve the quality of teaching. Our goal is to
produce innovative and meaningful conversations between teachers and students,
create standards for evaluation, and improve the efficacy of AI-for-Education
initiatives. In Section 3, we discuss the challenges of utilizing existing LLMs
to effectively complete the educated tasks and present a unified framework for
addressing diverse education dataset, processing lengthy conversations, and
condensing information to better accomplish more downstream tasks. In Section
4, we summarize the pivoting tasks including Teacher-Student Dialogue
Auto-Completion, Expert Teaching Knowledge and Style Transfer, and Assessment
of AI-Generated Content (AIGC), providing a clear path for future research. In
Section 5, we also explore the use of external and adjustable LLMs to improve
the generated content through human-in-the-loop supervision and reinforcement
learning. Ultimately, this paper seeks to highlight the potential for AI to aid
the field of education and promote its further exploration.

利用人工智能的互动场景增强课堂教学品质，包括对话自动完成、知识和风格传递，和评估人工智能生成的内容，探索多种应用和挑战，为未来研究提供清晰的道路。