ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Learning Innovations
This article is part of the Research TopicArtificial Intelligence in Educational Technology: Innovations, Impacts, and Future DirectionsView all 23 articles
When ChatGPT Joins the Team: A Mixed-Methods Study of AI-Mediated Collaborative Lesson Design
Provisionally accepted
- 1Universita degli Studi di Trento Dipartimento di Psicologia e Scienze Cognitive, Rovereto, Italy
- 2Università degli Studi di Genova Settore innovazione didattica, sviluppo e certificazione delle competenze, Genoa, Italy
- 3National Board of Medical Examiners, Philadelphia, United States
- 4CUNY Graduate Center, New York, United States
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The application and influence of artificial intelligence (AI), and specifically Large Language Models (LLMs), in educational processes is widely discussed. However, there remains a gap in research on using LLMs as peer-like contributors in collaborative learning contexts. This article reports a mixed-methods quasi-experimental study investigating how positioning ChatGPT as a peer-like feedback provider shapes student-teachers' learning and collaboration during group lesson-design activities. The study employed a counterbalanced crossover structure for knowledge assessment and a sequential two-task design for authentic artefact production. A total of 102 teachers in training (M_age = 38.87, SD = 8.01), organized into 21 groups, completed two authentic design tasks within a single session. Across the session, students progressively adapted to AI interaction, refining how they queried the model and how they evaluated and integrated its suggestions. Results indicate a Post-Withdrawal Sustained Performance (PWSP) effect: improvements observed during AI-available phases were not followed by a detectable decline in the immediately subsequent AI-withdrawn phase within the study timeframe. This pattern was clearest for technology-related knowledge and was consistent with stable artefact quality after AI removal. While ChatGPT support increased efficiency and contributed to technology-focused insights, qualitative evidence also pointed to tensions, including reduced peer-to-peer idea-building in some groups and concerns about creativity. Overall, the findings suggest that integrating LLMs as a feedback team-mate can support collaborative design work without immediate post-withdrawal performance costs, particularly when learners are scaffolded to engage critically with AI output rather than accept it unreflectively.
Keywords: AI in education1, AI-in-the-Loop6, artificial intelligence3, Computer-Supported Collaborative Learning5, Educational Feedback2, Educational Technology8, human-computer interaction4, Large Language Models7
Received: 21 Nov 2025; Accepted: 16 Feb 2026.
Copyright: © 2026 Agostini, Serbati, Picasso and Lipnevich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Daniele Agostini
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