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With the proliferation of AI, there is a growing concern regarding individuals becoming overly reliant on AI, leading to a decrease in intrinsic skills and autonomy. Assistive AI frameworks, on the other hand, also have the potential to improve human learning and performance by providing personalized learning experiences and real-time feedback. To study these opposing viewpoints on the consequences of AI assistance, we conducted a behavioral experiment using a dynamic decision-making game to assess how AI assistance impacts user performance, skill transfer, and cognitive engagement in task execution. Participants were assigned to one of four conditions that featured AI assistance at different time-points during the task. Our results suggest that AI assistance can improve immediate task performance without inducing human skill degradation or carryover effects in human learning. This observation has important implications for AI assistive frameworks as it suggests that there are classes of tasks in which assistance can be provided without risking the autonomy of the user. We discuss the possible reasons for this set of effects and explore their implications for future research directives.
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This content will become publicly available on May 1, 2027
Offline Reinforcement Learning for Adaptive Support in AI-Assisted Decision-Making
AI decision-support tools typically offer a fixed type of assistance, like AI recommendations and explanations, regardless of the specific decision, individual, or broader context. This fixed design has been shown to hinder both human-AI decision accuracy and human skill improvement in the task. We posit that AI assistance needs to be dynamic, changing in response to contextual factors (e.g., AI uncertainty, task difficulty), individual differences, and specified objectives (e.g., decision accuracy, skill improvement). To enable such adaptive support, we propose reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize human-AI interaction for diverse objectives. RL enables optimizing various objectives in AI-assisted decision-making by tailoring and adaptively providing decision support to humans --- the right type of assistance, to the right person, at the right time. We instantiated our approach with two objectives: human-AI accuracy on the decision-making task and human skill improvement (i.e., learning about the task) and learned decision support policies from previous human-AI interaction data. We compared the optimized policies against several baselines in AI-assisted decision-making. Across two experiments (N = 316 and N = 964), our results consistently demonstrated that people interacting with policies optimized for accuracy achieve significantly higher accuracy --- and even human-AI complementarity --- compared to those interacting with any other type of AI support. Our results further indicated that human learning was more difficult to optimize than accuracy. While the policies learned the best available actions to optimize learning, participants who interacted with learning-optimized policies showed significant learning improvement only at times. Our research (1) demonstrates offline RL to be a promising approach to model the dynamics of human-AI decision-making, leading to policies that may optimize various objectives and provide novel insights about the AI-assisted decision-making space, and (2) emphasizes the importance of considering skill improvement and other human-centric objectives beyond accuracy in AI-assisted decision-making, opening up the novel research challenge of optimizing human-AI interaction for such objectives.
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- Award ID(s):
- 2107391
- PAR ID:
- 10680320
- Publisher / Repository:
- ACM Press
- Date Published:
- Journal Name:
- ACM Transactions on Computer-Human Interaction
- ISSN:
- 1073-0516
- Subject(s) / Keyword(s):
- AI-assisted decision-making overreliance human-centric objectives human-centered AI explainable AI human-AI interaction decision support systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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