Surgical education • simulations • emotion regulation • medical technology • team training
My research aims to enhance surgical and medical education by reducing adverse events and inefficiencies, especially those associated with the incidence of undesirable and unregulated emotions and burnout. In my lab, we apply psychological and educational theories using interdisciplinary research methods and leverage advanced technologies, including augmented reality (AR), virtual reality (VR) and artificial intelligence (AI), to accomplish these aims. My interdisciplinary research program draws on mixed methods (quantitative and qualitative) that include both objective (e.g., skin conductance, facial recognition software, eyetracking) and subjective (self-report instruments, semi-structured interviews) measures of emotion and cognition that help us assess a variety of surgical and medical competencies.
Harley, J.M., Pekrun, R., Taxer, J.L., & Gross, J.J. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(2), 106-126. DOI: 10.1080/00461520.2019.1587297.
Harley, J.M., Jarrell, A., & Lajoie, S.P. (2019). Emotion regulation tendencies, achievement emotions, and physiological arousal in a medical diagnostic reasoning simulation. Instructional Science, 47(2), 151-180. DOI: 10.1007/s11251-018-09480-z.
Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2018). “Let’s set up some subgoals”: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11(1), 54-66. DOI: 10.1109/TLT.2017.2756629.
Harley, J.M., Lajoie, S. P., Frasson, C., Hall, N.C., & (2017). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education, 27(2), 268-297. DOI: 10.1007/s40593-016-0126-8.
Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615-625. DOI: 10.1016/j.chb.2015.02.013.