Transforming therapist education through intelligent supervision technology
Training therapists traditionally relies on limited supervision hours and delayed feedback. Trainees often wait days or weeks to review sessions, missing crucial learning moments and extending the path to clinical competence.
We developed an AI Supervisor Agent that analyzes therapy sessions through multiple input channels—written session notes, clinical summaries, and when permitted, audio or video recordings. Working with senior clinical supervisors, we encoded decades of supervisory expertise into an AI system that augments human supervision.
The Supervisor Agent provides multi-dimensional analysis across various input formats:
Evaluates rapport, empathy, and alliance formation from session documentation
Detects incongruence and flags scripted vs. genuine responses
Recognizes modalities used, evaluates timing, suggests alternatives
Identifies missed opportunities and provides actionable improvements
The system processes whatever documentation format is available—from brief session notes to full recordings—generating comprehensive feedback reports with specific examples and targeted recommendations.
Effective AI supervision requires highlighting 3-5 key learning moments rather than exhaustive critique. Success comes from positioning the AI as a supportive training companion, not an evaluative judge—significantly improving adoption and learning outcomes when framed correctly.
Transformed therapist training by providing immediate, comprehensive feedback and reducing time to clinical competence while maintaining the human element essential to therapeutic supervision.