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Dynamic conversational agent-based support for collaborative learning has shown significant positive effects on learning over no-support or static-support control conditions in prior studies. In order to understand the boundary between human-led and AI-led support for collaboration, we compare in this study an approach where the agent’s primary role is to help students regulate their own collaboration with two more typical prompting strategies that are used only during a reflection phase: one designed to provide a specific informational focus for the reflection, and the other designed to draw out evaluation, elaboration, and exploration of alternative perspectives. Significant positive effects on learning over and above just the human-led form of support are observed when either of the prompting strategies are used.
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This content will become publicly available on April 11, 2026
A No Free Lunch Theorem for Human-AI Collaboration
The gold standard in human-AI collaboration is complementarity: when combined performance exceeds both the human and algorithm alone. We investigate this challenge in binary classification settings where the goal is to maximize 0-1 accuracy. Given two or more agents who can make calibrated probabilistic predictions, we show a No Free Lunch-style result. Any deterministic collaboration strategy (a function mapping calibrated probabilities into binary classifications) that does not essentially always defer to the same agent will sometimes perform worse than the least accurate agent. In other words, complementarity cannot be achieved for free. The result does suggest one model of collaboration with guarantees, where one agent identifies obvious errors of the other agent. We also use the result to understand the necessary conditions enabling the success of other collaboration techniques, providing guidance to human-AI collaboration.
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- Award ID(s):
- 2339427
- PAR ID:
- 10616972
- Publisher / Repository:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 39
- Issue:
- 13
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 14369 to 14376
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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