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.

数字教育在近十年来变得越来越流行，尤其是在 COVID-19 大流行之后。本文综述了以往关于提示生成的研究，旨在填补教育和认知科学以及人工智能和自然语言处理研究之间的鸿沟，提出了提示生成任务的形式化定义，并讨论与该定义相一致的有效提示生成系统的构建路线图，包括挑战、未来方向和伦理考虑。

引导生成研究领域的导航：从过去到未来

Navigating the Landscape of Hint Generation Research: From the Past to  the Future

Nowadays, individuals tend to engage in dialogues with Large Language Models,
seeking answers to their questions. In times when such answers are readily
accessible to anyone, the stimulation and preservation of human's cognitive
abilities, as well as the assurance of maintaining good reasoning skills by
humans becomes crucial. This study addresses such needs by proposing hints
(instead of final answers or before giving answers) as a viable solution. We
introduce a framework for the automatic hint generation for factoid questions,
employing it to construct TriviaHG, a novel large-scale dataset featuring
160,230 hints corresponding to 16,645 questions from the TriviaQA dataset.
Additionally, we present an automatic evaluation method that measures the
Convergence and Familiarity quality attributes of hints. To evaluate the
TriviaHG dataset and the proposed evaluation method, we enlisted 10 individuals
to annotate 2,791 hints and tasked 6 humans with answering questions using the
provided hints. The effectiveness of hints varied, with success rates of 96%,
78%, and 36% for questions with easy, medium, and hard answers, respectively.
Moreover, the proposed automatic evaluation methods showed a robust correlation
with annotators' results. Conclusively, the findings highlight three key
insights: the facilitative role of hints in resolving unknown questions, the
dependence of hint quality on answer difficulty, and the feasibility of
employing automatic evaluation methods for hint assessment.

该研究提出了一种通过生成提示来辅助回答问题的方法，并介绍了一个大规模数据集 TriviaHG，其中包含了 160,230 个提示对应于 16,645 个问题。此外，研究还提出了一种自动评估方法来衡量提示的有效性，研究结果表明提示对解决未知问题具有促进作用，提示质量依赖于答案的难易程度，并且可以使用自动评估方法进行提示评估。