Digital education has gained popularity in the last decade, especially after the COVID-19 pandemic. With the improving capabilities of large language models to reason and communicate with users, envisioning intelligent tutoring systems (ITSs) that can facilitate self-learning is not very far-fetched. One integral component to fulfill this vision is the ability to give accurate and effective feedback via hints to scaffold the learning process. In this survey article, we present a comprehensive review of prior research on hint generation, aiming to bridge the gap between research in education and cognitive science, and research in AI and Natural Language Processing. Informed by our findings, we propose a formal definition of the hint generation task, and discuss the roadmap of building an effective hint generation system aligned with the formal definition, including open challenges, future directions and ethical considerations.

翻译：数字教育在过去十年中日益普及，特别是在COVID-19疫情之后。随着大型语言模型推理和与用户交流能力的不断提升，构建能够促进自主学习的智能导学系统已非遥不可及。实现这一愿景的关键组成部分，是通过提示提供准确有效的反馈来支撑学习过程。本综述文章对提示生成领域的现有研究进行了全面回顾，旨在弥合教育与认知科学研究同人工智能与自然语言处理研究之间的鸿沟。基于研究发现，我们提出了提示生成任务的正式定义，并讨论了构建符合该定义的提示生成系统的路线图，包括开放挑战、未来方向与伦理考量。