Kristi Hape

University of Southern Indiana, United States

Title: Advancing Student Skills in Therapeutic Use of Self Using Artificial Intelligence and Biometric Feedback

---

Biography

Kristi L Hape is an Assistant Professor at the University of Southern Indiana (USI), where she is dedicated to educating and mentoring future healthcare professionals. Her work focuses on fostering student success through innovative teaching, evidence-based practice, and meaningful engagement in the classroom and clinical settings. She teaches within both the occupational therapy and occupational therapy assistant programs with specialty areas in pediatrics and community-based and inpatient mental health. With a passion for developing competent and compassionate practitioners, she emphasizes critical thinking, professional growth, and lifelong learning. At USI, Kristi collaborates with colleagues and community partners to advance educational excellence and enhance opportunities for student development. Through her commitment to teaching, mentorship, and service, she strives to positively impact both her students and the broader healthcare community.

Abstract

This research demonstrates how AI-powered facial analysis can be used to teach occupational therapy students to apply the therapeutic use of self within the telehealth environment. This builds on extant research utilizing facial expression analysis to teach the influence of human emotional responses (Jiang, et al., 2021; Kulke et al., 2020).

As telehealth continues to play an increasing role in healthcare delivery, occupational therapy educators must identify innovative methods to help students develop therapeutic communication skills in virtual settings. Traditional instruction often relies on observation and faculty feedback; however, emerging technologies provide opportunities for more objective and individualized learning experiences. Facial expression analysis offers a unique way to capture and evaluate emotional responses that may otherwise go unnoticed during telehealth interactions. By making these responses visible to learners, students can gain deeper insight into the impact of their verbal and nonverbal communication.

Emotions were measured using IMOTIONS® Artificial Intelligence (AI) technology. Students' telehealth videos were recorded and analyzed using facial expression analysis to identify both the client and student non-verbal cues. This process allowed students to see how their words produced joy in their client and how joy is mirrored between the student and the client. Through guided reflection on these findings, students gained greater appreciation for the therapeutic use of self and its role in establishing rapport, engagement, and trust within the telehealth environment. This technology hasadditional application opportunities that could be applied to explore emotional display of non-verbal cues on screen to enhance non-verbal communication for both providers and people with autism.