Artificial intelligence is increasingly used to support decision making in healthcare, especially in time-sensitive settings such as emergency care and intensive care units. However, people do not always rely on these systems appropriately. Some users may place too much trust in incorrect recommendations, while others may ignore useful guidance. These mismatches can affect decision quality and patient safety. This project studies how people interact with artificial intelligence in such settings and explores ways to support more appropriate use. By improving how clinicians interpret and respond to artificial intelligence, the work aims to support safer and more reliable decision making. The project also contributes to education by engaging students in simulation-based learning and providing training opportunities in human-centered artificial intelligence. This project develops a framework to study how trust in artificial intelligence changes over time during decision making. The research combines behavioral data with physiological signals, including eye movements and brain activity, to better understand user responses. First, a mathematical model is developed to represent trust as a changing internal state influenced by task conditions and system performance. Second, machine learning methods are used to estimate this state in real time using data collected from clinicians interacting with simulated clinical scenarios. Third, the project explores interface strategies that pr