Teachable Agents and the Protégé Effect: Increasing the Effort Towards Learning
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Learners' sense of responsibility was exerted towards the intelligent agent, consistent with research demonstrating that students may work harder on behalf of their agents than they would for themselves.
“More than that, learners' sense of responsibility was exerted towards the intelligent agent, which was consistent with the research from Chase et al., demonstrating that students may work harder on behalf of their agents than they would for themselves [53].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [53]
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References 34 · Cited by 347
- Authors
- Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L.
- Affiliations
- Stanford University (US)
- Corresponding author
- Chase, Catherine C.
- Venue
- Journal of Science Education and Technology
- Published
- 2009-06-30
- Volume
- 18
- Issue
- 4
- Pages
- 334-352
- ISSN
- 1059-0145 (ISSN-L)
- 1573-1839
- OpenAlex
- W2053976921
- Cited by
- 347
- References
- 34
- Citations by year
- 2026: 43 · 2025: 27 · 2024: 19 · 2023: 19 · 2022: 25 · 2021: 24 · 2020: 23 · 2019: 22 · 2018: 22 · 2017: 17 · 2016: 18 · 2015: 19 · 2014: 17 · 2013: 21 · 2012: 14
- FWCI
- 10.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Science Education and Technology (publishedVersion)
- Funders
- National Science Foundation
- 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
- teachable agents, social learning, learning effort, student motivation, mental state attribution, attribution of responsibility, failure attribution, verbal protocols, lower-achieving students, biology learning, computer-based learning environments, ego protection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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