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.

翻译：本文提出了一系列利用人工智能增强课堂教学的交互场景，例如对话自动补全、知识与风格迁移，以及人工智能生成内容的评估。通过借助大型语言模型的最新发展，我们探索了人工智能增强和丰富师生对话、提升教学质量的潜力。我们的目标是促成师生之间具有创新性和意义的对话，建立评估标准，并提高人工智能赋能教育倡议的效果。在第3节中，我们讨论了利用现有大型语言模型有效完成教育任务所面临的挑战，并提出一个统一框架，用于处理多样化教育数据集、处理长篇幅对话以及压缩信息，以便更好地完成更多下游任务。在第4节中，我们总结了关键任务，包括师生对话自动补全、专家教学知识与风格迁移，以及人工智能生成内容的评估，为未来研究提供了清晰的路径。在第5节中，我们还探索了通过人在回路监督和强化学习，利用外部可调节的大型语言模型来改进生成内容。最终，本文旨在强调人工智能助力教育领域的潜力，并推动对其进一步探索。