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

April 06, 2024 ยท Declared Dead ยท ๐Ÿ› Transactions of the Association for Computational Linguistics

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Authors Anubhav Jangra, Jamshid Mozafari, Adam Jatowt, Smaranda Muresan arXiv ID 2404.04728 Category cs.CL: Computation & Language Cross-listed cs.HC Citations 8 Venue Transactions of the Association for Computational Linguistics Last Checked 4 months ago
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.
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