Tasks given to Participants of an Adaptive Trust-Calibrated Human–AI Collaboration for Enhanced System Usability

The task dataset used in this study comprises 120 decision-support tasks designed to reflect realistic Human–AI collaboration scenarios under varying levels of complexity and uncertainty. Each task is characterized by a unique identifier, task type (risk assessment, classification, decision support, or what-if analysis), difficulty level (low, medium, high), simulated AI reliability condition (high, medium, low), and an associated ground-truth label to enable objective performance evaluation. The dataset was systematically constructed to expose participants to diverse interaction conditions, including cases where AI recommendations are reliable and others where uncertainty is deliberately introduced, thereby enabling observation of over-reliance and under-reliance behaviors. By explicitly modeling task difficulty and AI reliability, the dataset supports controlled analysis of trust calibration, cognitive workload, and decision accuracy, and provides a reproducible foundation for evaluating adaptive Human–AI collaboration frameworks without reliance on sensitive real-world data.