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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01205 |
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| _version_ | 1866917355937857536 |
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| author | Jin, Yueqiao Martinez-Maldonado, Roberto Shi, Wanruo Huang, Songjie Zheng, Mingmin Han, Xinbin Gasevic, Dragan Yan, Lixiang |
| author_facet | Jin, Yueqiao Martinez-Maldonado, Roberto Shi, Wanruo Huang, Songjie Zheng, Mingmin Han, Xinbin Gasevic, Dragan Yan, Lixiang |
| contents | Generative AI is increasingly positioned as a peer in collaborative learning, yet its effects on ethical deliberation remain unclear. We report a between-subjects experiment with university students (N=217) who discussed an autonomous-vehicle dilemma in triads under three conditions: human-only control, supportive AI teammate, or contrarian AI teammate. Using moral foundations lexicons, argumentative coding from the augmentative knowledge construction framework, semantic trajectory modelling with BERTopic and dynamic time warping, and epistemic network analysis, we traced how AI personas reshape moral discourse. Supportive AIs increased grounded/qualified claims relative to control, consolidating integrative reasoning around care/fairness, while contrarian AIs modestly broadened moral framing and sustained value pluralism. Both AI conditions reduced thematic drift compared with human-only groups, indicating more stable topical focus. Post-discussion justification complexity was only weakly predicted by moral framing and reasoning quality, and shifts in final moral decisions were driven primarily by participants' initial stance rather than condition. Overall, AI teammates altered the process, the distribution and connection of moral frames and argument quality, more than the outcome of moral choice, highlighting the potential of generative AI agents as teammates for eliciting reflective, pluralistic moral reasoning in collaborative learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Machines Join the Moral Circle: The Persona Effect of Generative AI Agents in Collaborative Reasoning Jin, Yueqiao Martinez-Maldonado, Roberto Shi, Wanruo Huang, Songjie Zheng, Mingmin Han, Xinbin Gasevic, Dragan Yan, Lixiang Human-Computer Interaction Generative AI is increasingly positioned as a peer in collaborative learning, yet its effects on ethical deliberation remain unclear. We report a between-subjects experiment with university students (N=217) who discussed an autonomous-vehicle dilemma in triads under three conditions: human-only control, supportive AI teammate, or contrarian AI teammate. Using moral foundations lexicons, argumentative coding from the augmentative knowledge construction framework, semantic trajectory modelling with BERTopic and dynamic time warping, and epistemic network analysis, we traced how AI personas reshape moral discourse. Supportive AIs increased grounded/qualified claims relative to control, consolidating integrative reasoning around care/fairness, while contrarian AIs modestly broadened moral framing and sustained value pluralism. Both AI conditions reduced thematic drift compared with human-only groups, indicating more stable topical focus. Post-discussion justification complexity was only weakly predicted by moral framing and reasoning quality, and shifts in final moral decisions were driven primarily by participants' initial stance rather than condition. Overall, AI teammates altered the process, the distribution and connection of moral frames and argument quality, more than the outcome of moral choice, highlighting the potential of generative AI agents as teammates for eliciting reflective, pluralistic moral reasoning in collaborative learning. |
| title | When Machines Join the Moral Circle: The Persona Effect of Generative AI Agents in Collaborative Reasoning |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.01205 |