A Survey of Automated Programming Hint Generation -- The HINTS Framework
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Abstract (arXiv)
Automated tutoring systems offer the flexibility and scalability necessary to facilitate the provision of high quality and universally accessible programming education. In order to realise the full potential of these systems, recent work has proposed a diverse range of techniques for automatically generating hints to assist students with programming exercises. This paper integrates these apparently disparate approaches into a coherent whole. Specifically, it emphasises that all hint techniques can be understood as a series of simpler components with similar properties. Using this insight, it presents a simple framework for describing such techniques, the Hint Iteration by Narrow-down and Transformation Steps (HINTS) framework, and it surveys recent work in the context of this framework. It discusses important implications of the survey and framework, including the need to further develop evaluation methods and the importance of considering hint technique components when designing, communicating and evaluating hint systems. Ultimately, this paper is designed to facilitate future opportunities for the development, extension and comparison of automated programming hint techniques in order to maximise their educational potential.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Aug 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 4; 2025: 2; 2026: 1.
11 papers.
- 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
- 2023Can Language Models Employ the Socratic Method? Experiments with Code Debuggingcited by 14
- 2024TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questionscited by 7
- 2024Navigating the Landscape of Hint Generation Research: From the Past to the Futurecited by 6
- 2022Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systemscited by 4
- 2024WikiHint: A Human-Annotated Dataset for Hint Ranking and Generationcited by 4
- 2025HintEval: An Open-Source Python Toolkit for Hint Generation and Hint Evaluationcited by 3
- 2024On the Opportunities of Large Language Models for Programming Process Datacited by 1
- 2021ast2vec: Utilizing Recursive Neural Encodings of Python Programscited by 0
- 2025Designing and Evaluating Chain-of-Hints for Scientific Question Answeringcited by 0
- 2026SCRIPT: Implementing an Intelligent Tutoring System for Programming in a German University Contextcited by 0
- 2026SCRIPT: Implementing an Intelligent Tutoring System for Programming in a German University Context17 Apr 2026
- 2025Designing and Evaluating Chain-of-Hints for Scientific Question Answering24 Oct 2025
- 2025HintEval: An Open-Source Python Toolkit for Hint Generation and Hint Evaluation2 Feb 2025
- 2024WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation2 Dec 2024
- 2024On the Opportunities of Large Language Models for Programming Process Data1 Nov 2024
- 2024Navigating the Landscape of Hint Generation Research: From the Past to the Future6 Apr 2024
- 2024TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions27 Mar 2024
- 2023Can Language Models Employ the Socratic Method? Experiments with Code Debugging4 Oct 2023
- 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers5 Apr 2023
- 2022Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems8 Jun 2022
- 2021ast2vec: Utilizing Recursive Neural Encodings of Python Programs22 Mar 2021