[2502.16411] Predictive AI Can Support Human Learning while Preserving Error Diversity

Computer Science > Human-Computer Interaction arXiv:2502.16411 (cs) [Submitted on 23 Feb 2025 (v1), last revised 28 Feb 2026 (this version, v5)] Title:Predictive AI Can Support Human Learning while Preserving Error Diversity Authors:Vivianna Fang He, Sihan Li, Phanish Puranam, Feng Lin View a PDF of the paper titled Predictive AI Can Support Human Learning while Preserving Error Diversity, by Vivianna Fang He and Sihan Li and Phanish Puranam and Feng Lin View PDF Abstract:We examined the effects of predictive AI deployment on the immediate performance and learning of medical novices. In two pre-registered field experiments, we varied whether AI input was provided during the training or practice of lung cancer diagnoses, or both. Our results show that different AI deployments have distinct implications for human professionals. AI input during training or practice independently improves individuals' diagnostic accuracy, whereas deployment across both phases yields gains that exceed either approach alone. Furthermore, AI input in both training and earlier practice can improve the accuracy of individuals' subsequent independent diagnoses. Beyond individual accuracy, AI deployment affects the diversity of errors across individuals, with consequences for the accuracy of group decisions (e.g. when getting a second or third opinion on a diagnosis). Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2502.16411 [cs....