Hinting as a Tactic in One-on-One Tutoring
Abstract
One-on-one tutoring is a particularly effective mode of instruction, and we have studied the behavior of expert tutors in such a setting. A tactic commonly used by our expert tutors is hinting, that is, the prompting of a student to recollect information presumed to be known to him or her, or the prompting of a student to make an inference needed to solve a problem or answer a question, or both. Hints may directly convey information or may point to information the student already possesses. Another tactic prompts the student in a step-by-step manner (in a directed line of reasoning) to an answer. Our tutors generated 315 hints and directed lines of reasoning in 30 hr of tutoring. The surface structure of hints is complex and varied, reflecting, in part, the fact that the utterances making up hints often serve multiple functions. Hinting is triggered by student errors but ceases when it appears that the student is unable to respond appropriately. Hints encourage the student to engage in active cognitive processes that are thought to promote deeper understanding and long-term retention. It is our intention to apply our knowledge of tutorial dialogue generation to the building of an intelligent tutoring system (ITS).
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The standard hints design principle in ITS consists of three levels of increasing specificity: pointing hint, teaching hint, bottom-out hint.
“According to the standard hints design principle in ITS (VanLehn, 2006), the hint sequence consists of three levels of increasing specificity: 1) Pointing Hint: Directs the student's attention to a specific error or location without revealing the solution (Hume et al., 1996). 2) Teaching Hint: Provides the domain principle or rule required to solve the step (Hume et al., 1996). 3) Bottom-Out Hint:”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Hume et al., 1996
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VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
VIVID: Human-AI Collaborative Authoring of Vicarious Dialogues from Lecture Videos
Choi, S., Lee, H., Lee, Y., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642867 · Cited by 17 · see citing works
Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
Cao, J., Zhao, C. Q., Schunn, C., McLaughlin, E. A., Lin, J., & Koedinger, K. R. (2026) · arXiv · doi:10.48550/arxiv.2604.07469
- Authors
- Hume, G., Michael, J., Rovick, A., & Evens, M.
- Affiliations
- Valparaiso University (US)
- ORCID
- Michael, Joel
- Venue
- Journal of the Learning Sciences
- Publisher
- Informa UK Limited
- Published
- 1996-01
- Volume
- 5
- Issue
- 1
- Pages
- 23-47
- ISSN
- 1050-8406 (ISSN-L)
- 1532-7809
- OpenAlex
- W2016122903
- Cited by
- 144
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 2 · 2018: 8 · 2017: 3 · 2016: 4 · 2015: 6 · 2014: 2 · 2013: 4 · 2012: 4
- FWCI
- 9.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Learning Sciences (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- one-on-one tutoring, intelligent tutoring systems, student errors, cognitive processes, long-term retention
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