Career: calibrating human trust in artificial intelligence through real-time behavioral and physiological feedback in healthcare decision making

The National Science Foundation Division of Computing and Communication Foundations awarded West Virginia University Research Corporation $409,667 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop a framework for calibrating human trust in artificial intelligence during healthcare decision-making.

The project combines behavioral data with physiological signals—including eye movements and brain activity—to understand how clinicians interact with AI systems in time-sensitive settings such as emergency care and intensive care units. The research develops a mathematical model representing trust as a dynamic internal state, uses machine learning methods to estimate this state in real time from clinician interactions with simulated clinical scenarios, and explores interface strategies that provide targeted feedback to align user decisions with system reliability. The work addresses a critical gap: clinicians often place inappropriate trust in AI recommendations, either over-relying on incorrect guidance or ignoring useful assistance, both of which can degrade decision quality and patient safety. The project will generate data, models, and open resources to support future research on human-centered artificial intelligence and will engage students through simulation-based learning and training opportunities.

Performance occurs in Morgantown, West Virginia, with a period of performance from October 1, 2026, through September 30, 2031. This is a CAREER award, a Project Grant assistance type.

Abstract

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 provide targeted feedback to help users better align their decisions with the reliability of the system. These approaches are evaluated through controlled simulation studies with clinician participants. The project will generate data, models, and open resources to support future research on human interaction with artificial intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.