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arXiv · 2502.16411

Predictive AI Can Support Human Learning while Preserving Error Diversity

Vivianna Fang He·Sihan Li·Phanish Puranam·Feng Lin
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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).

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Vivianna Fang He, Sihan Li, Phanish Puranam, Feng Lin. 2025-02-23. Predictive AI Can Support Human Learning while Preserving Error Diversity. https://arxiv.org/abs/2502.16411

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