mlsanityData to 7 Oct 2026

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

arXiv 2404.04728 ↗First posted 6 Apr 2024cs.CL cs.HCcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 63 of its 185 references that are in the map. First posted on arXiv 6 Apr 2024.

Builds on

63 of 185 references.

References · 63

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38 more
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50 most cited of 63 All 63 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technologycited by 9
  2. 2024WikiHint: A Human-Annotated Dataset for Hint Ranking and Generationcited by 4
  3. 2025HintEval: An Open-Source Python Toolkit for Hint Generation and Hint Evaluationcited by 3
  4. 2025Designing and Evaluating Chain-of-Hints for Scientific Question Answeringcited by 0
  5. 2026Inferential Question Answeringcited by 0
  6. 2026Context Convergence Improves Answering Inferential Questionscited by 0