Beyond Awareness: Investigating How AI and Psychological Factors Shape Human Self-Confidence Calibration

TL;DR

This paper explores how AI and psychological traits like Need for Cognition and Actively Open-Minded Thinking influence human self-confidence calibration, decision accuracy, and AI-assisted decision-making effectiveness.

Contribution

It introduces two studies examining the impact of self-confidence calibration and psychological traits on decision outcomes and proposes design recommendations for personalized AI systems.

Findings

Calibrated self-confidence improves decision accuracy.

Psychological traits significantly affect confidence calibration.

Tailored AI support enhances user trust and decision quality.

Abstract

Human-AI collaboration outcomes depend strongly on human self-confidence calibration, which drives reliance or resistance toward AI's suggestions. This work presents two studies examining whether calibration of self-confidence before decision tasks, low versus high levels of Need for Cognition (NFC), and Actively Open-Minded Thinking (AOT), leads to differences in decision accuracy, self-confidence appropriateness during the tasks, and metacognitive perceptions (global and affective). The first study presents strategies to identify well-calibrated users, also comparing decision accuracy and the appropriateness of self-confidence across NFC and AOT levels. The second study investigates the effects of calibrated self-confidence in AI-assisted decision-making (no AI, two-stage AI, and personalized AI), also considering different NFC and AOT levels. Our results show the importance of human self-confidence calibration and psychological traits when designing AI-assisted decision systems. We further propose design recommendations to address the challenge of calibrating self-confidence and supporting tailored, user-centric AI that accounts for individual traits.