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

TL;DR

This survey reviews the evolution of hint generation research in intelligent tutoring systems, highlighting recent advances, challenges, and future directions to enhance AI-driven personalized education.

Contribution

It provides a formal definition of hint generation and bridges research in education, cognitive science, AI, and NLP for developing effective tutoring hints.

Findings

Comprehensive overview of hint generation methods

Identification of open challenges and ethical issues

Roadmap for future research in hint generation systems

Abstract

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.