  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.
