The Impact of Metacognitive AI on Appropriate Reliance in AI-Assisted Decision-Making: The Role of Trust Resilience and Critical Thinking
Bibliographic record
Abstract
As AI becomes increasingly integrated into high-stakes decision-making, ensuring appropriate reliance—users’ ability to calibrate their trust based on AI reliability—remains a critical challenge. Metacognitive AI, which monitors and regulates its own decision-making, has the potential to improve trust calibration by fostering trust resilience and critical thinking engagement. However, its enhanced self-reflective capabilities may also lead to over-reliance due to its perceived authority. Drawing on dual-process theory and obedience to authority theory, this study investigates how metacognitive AI influences user reliance behaviors. Using a between-subjects experimental design, 200 participants will interact with AI advisors exhibiting high or low metacognitive ability in a medical diagnosis task. We examine the effects on trust resilience, critical thinking, and reliance patterns, moderated by Need for Cognition (NFC). Findings will contribute to the design of AI systems that foster appropriate reliance and decision-making autonomy, reducing automation bias and improving human-AI collaboration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".