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Abstract PurposeMeasurable results of efforts to teach empathy to engineering students are sparse and somewhat mixed. This study’s objectives are (O1) to understand how empathy training affects students’ professional development relative to other educational experiences, (O2) to track empathy changes due to training over multiple years, and (O3) to understand how and what students learn in empathy training environments. MethodsStudents in a multiple-semester empathy course completed surveys ranking the career development impact of the empathy program against other college experiences (O1), rating learning of specific empathy skills (O2), and ranking program elements’ impact on empathy skills (O3). Intervention and control groups completed the Interpersonal Reactivity Index and Jefferson Scale of Empathy at four time points (O2). Cohort students participated in post-program interviews (O1, O3). ResultsO1: Empathy training impacted career development more than several typical college activities but less than courses in major. O2: Students reported gains in four taught empathy skills. Cohort students showed significant increases in the Jefferson Scale while the control group did not. There were no significant changes in Interpersonal Reactivity Index scores. O3: interactive exercises had a significant effect on students’ learning all empathy skills while interactions with people with disabilities had significant effect on learning to encounter others with genuineness. Students valued building a safe in-class community facilitating their success in experiential environments. ConclusionsThis study highlights empathy skills’ importance in engineering students’ development, shows gains in empathy with training, and uncovers key factors in students’ learning experience that can be incorporated into engineering curricula. 

Functional electrical stimulation is a promising technique for restoring arm function to those with paralysis from a high spinal cord injury. While simple controllers are easy to implement, model-based controllers are likely better equipped to leverage the arm’s kinematic and dynamic complexity, particularly for the high variations associated with functional arm movement. One modelling technique for a model-based controller is Gaussian Process Regression. Previous simulation work has shown promise leveraging whole-arm error data to identify the arm’s various subsystems, but used perfect simulated data. We asked caregivers to correct a robotic arm’s movement as simulated muscles generated torque. The simulated muscles were controlled as if they were electrically stimulated human arm muscles. This study demonstrates non-expert caregivers’ ability to collect this error data via whole-arm corrections, and provides insight into their ability to improve arm subsystem models made with Gaussian Process Regression. Despite significant error in caregivers’ ability to provide force corrections to hold the robot in a static configuration, these corrections were leveraged to significantly improve muscle models; the muscles that improved the most were the ones primarily used to move the physiologically actuated robot.