Piecing Together Teamwork: A Responsible Approach to an LLM-based Educational Jigsaw Agent
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Research in social-emotional skills and collaboration targets teamwork and social responsibility by building empathy and collective efficacy during negotiation, joint decision making, and supportive communication.
“Research in this category targets teamwork and social responsibility by building empathy and collective efficacy during negotiation, joint decision making, and supportive communication, thereby encouraging active engagement with social issues [119,120].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [120]
At the relational level, AI can foster more positive peer affect and teamwork and is positively appraised by parents and teachers for enabling parent-child dialogue and instructional support.
“At the relational level, AI can foster more positive peer affect and teamwork [107,120] and is positively appraised by parents and teachers for enabling parent-child dialogue and instructional support [71,121,123].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [120]
Cites
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1× · see citing works
“It is difcult to instill this tacit knowledge into AI systems [10] as “human reasoning is embodied, situated, in a social context and involves actions, often improvised, in the world, the complexity of which formal models cannot replicate” [11].”
The Construction of Shared Knowledge in Collaborative Problem Solving.
Roschelle, J., & Teasley, S. D. (1995) · Computer Supported Collaborative Learning · doi:10.1007/978-3-642-85098-1_5 · Cited by 2,274 · cited 1× · see citing works
“This includes the examination of students’ creation of a shared problem space [4, 69], how they contribute to this space, and build on each other’s ideas [71, 83], to solve problems together [21].”
Power to the People: The Role of Humans in Interactive Machine Learning
Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014) · AI Magazine · doi:10.1609/aimag.v35i4.2513 · Cited by 1,084 · see citing works
Interacting with educational chatbots: A systematic review
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11177-3 · Cited by 841 · see citing works
HellaSwag: Can a Machine Really Finish Your Sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1472 · Cited by 830 · cited 1× · see citing works
“Mistral-7B outperforms other LLMs of comparable size, including Meta’s Llama 2 [92], on a variety of language task benchmarks such as Hellaswag for commonsense reasoning and GSM8K for solving math word problems [96, 100].”
Future research recommendations for transforming higher education with generative AI
Chiu, T. K. F. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100197 · Cited by 511 · see citing works
Advancing the Science of Collaborative Problem Solving
Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018) · Psychological Science in the Public Interest · doi:10.1177/1529100618808244 · Cited by 390 · see citing works
What is Human-Centered about Human-Centered AI? A Map of the Research Landscape
Capel, T., & Brereton, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580959 · Cited by 228 · see citing works
Collaborative Problem-Solving Education for the Twenty-First-Century Workforce
Fiore, S. M., Graesser, A., & Greiff, S. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0363-y · Cited by 113 · see citing works
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Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
- Authors
- Doherty, E., Perkoff, E. M., von Bayern, S., Zhang, R., Dey, I., Bodzianowski, M., Puntambekar, S., & Hirshfield, L.
- Affiliations
- University of Colorado Boulder (US)
- University of Colorado Boulder, Boulder, Colorado, USA
- University of Wisconsin–Madison (US)
- ORCID
- Venue
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-17
- OpenAlex
- W4409750200
- Cited by
- 12
- References
- 10
- Citations by year
- 2026: 9 · 2025: 3
- FWCI
- 66.19
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- Funders
- NSF (National Science Foundation) (DRL 2019805)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- large language models, educational agents, teamwork, Jigsaw cooperative learning, cooperative learning, responsible AI, AI-assisted learning
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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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