Honors Theses
Date of Award
Spring 5-7-2026
Document Type
Undergraduate Thesis
Department
Psychology
First Advisor
John Young
Second Advisor
Robert Corban
Third Advisor
Donovan Wishon
Relational Format
Dissertation/Thesis
Abstract
Training competent psychotherapists requires substantial time, supervision, and performance-based feedback. However, traditional methods of providing feedback are resource-intensive and difficult to sustain outside of university and research settings. Recent advances in artificial intelligence (AI) have created new opportunities to automate aspects of psychotherapy fidelity assessment and provide trainees with more frequent feedback. The present study examines the implementation of Lyssn, an AI-based psychotherapy platform that provides clinical feedback to therapists, in a graduate-level course on Evidence-Based Services. Multiple years of course data were reviewed to evaluate therapist competence, therapist behaviors, and student engagement with AI-generated feedback. Particular emphasis was placed on Cognitive Therapy Rating Scale (CTRS) scores, Motivational Interviewing Skills Code (MISC) variables, and differences across class cohorts as Lyssn became more and more integrated into the class over the course of several years. Results indicated that students that were exposed to more frequent AI-generated feedback and actively encouraged to use may have demonstrated greater improvement in therapist competence over time. Specifically, the 2024 class cohort began the semester with lower baseline CTRS scores than other years but exhibited a steeper rate of improvement and achieved higher competence ratings by the end of the course. These findings are consistent with literature that emphasizes the importance of feedback and deliberate practice in skill development for psychotherapeutic practice. The results from this study suggest that artificial intelligence may have the ability to provide a scalable method for promoting and increasing immediate access to performance-based feedback while simultaneously reducing the burden associated with traditional supervision and fidelity assessment. Implications for psychotherapy training, evidence-based practice, and future applications of artificial intelligence in clinical education are discussed.
Recommended Citation
McCord, Beckham G., "Exploration of AI-based Method Feedback for Clinical Psychology Skill Development with an Emphasis on Deliberate Practice" (2026). Honors Theses. 3602.
https://egrove.olemiss.edu/hon_thesis/3602