  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.
