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December 6, 2025User Modeling and User-Adapted InteractionOpen Access

Exploring the impact of explainable AI and cognitive capabilities on users’ decisions

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Authors

FCFederico Maria CauLSLucio Davide Spano

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Overview

Observational analysis revealed that prediction accuracy and cognitive load differ based on AI explanations in users with varying Need for Cognition.

Key Points

  • High AI confidence increases reliance on AI while reducing cognitive load, and different explanations impact decision accuracy.
  • Both feature-based and counterfactual explanations were evaluated in relation to user cognitive load and accuracy outcomes.
  • Assessment using user-centric personalization focuses on adapting AI explanations to individual cognitive characteristics and task contexts.
  • These findings highlight the need for optimizing human–AI collaboration through tailored explanation styles for better decision-making.

Cite This Study

Cau et al. (2025) studied this question.

synapsesocial.com/papers/694020fd2d562116f28fb53ahttps://doi.org/10.1007/s11257-025-09438-0
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