Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University - Blease, Charlotte; Kharko, Anna; Annoni, Marco; Gaab, Jens; Locher, Cosima (Frontiers in Public Health, 2021)
Background: There is increasing use of psychotherapy apps in mental health care. Objective: This mixed methods pilot study aimed to explore postgraduate clinical psychology students’ familiarity and formal exposure to topics related to artificial intelligence and ma-chine learning (AI/ML) during their studies. Methods: In April-June 2020, we conducted a mixed-methods online survey using a conven-ience sample of 120 clinical psychology students enrolled in a two-year Masters’ program at a Swiss university. Results: In total 37 students responded (response rate: 37/120, 31%). Among respondents, 73% (n=27) intended to enter a mental health profession, and 97% reported that they had heard of the term ‘machine learning’. Students estimated 0.52% of their program would be spent on AI/ML education. Around half (46%) reported that they intended to learn about AI/ML as it pertained to mental health care. On 5-point Likert scale, students “moderately agreed” (median=4) that AI/M should be part of clinical psychology/psychotherapy education. Qualitative analysis of students’ comments resulted in 4 major themes on the impact of AI/ML on mental healthcare: (1) Changes in the quality and understanding of psychotherapy care; (2) Impact on patient-therapist interactions; (3) Impact on the psychotherapy profession; (4) Data management and ethical issues. Conclusions: This pilot study found that postgraduate clinical psychology students held a wide range of opinions but had limited formal education on how AI/ML-enabled tools might impact psychotherapy. The survey raises questions about how curricula could be enhanced to educate clinical psychology/psychotherapy trainees about the scope of AI/ML in mental healthcare.