Machine Learning in Clinical Psychology and Psychotherapy Education: A Survey of Postgraduate Students at a Swiss University

Blease, C. R.; Kharko, A. Y.; Annoni, M.; Gaab, J.; Locher, C.

Background: There is increasing use of for machine learning-enabled tools (e.g., psychotherapy apps) in mental health care. Objective: This study aimed to explore postgraduate clinical psychology and psychotherapy students' familiarity and formal exposure to topics related to artificial intelligence and machine learning (AI/ML) during their studies. Methods: In April-June 2020, we conducted a mixed-methods web-based survey using a convenience sample of 120 clinical psychology and psychotherapy enrolled in a two-year Masters' program students at a Swiss university. Results: In total 37 students responded (response rate: 37/120, 31%). Among the respondents, 73% (n=27) intended to enter a mental health profession. Among the students 97% reported that they had heard of the term 'machine learning,' and 78% reported that they were familiar with the concept of 'big data analytics'. Students estimated 18.61/3600 hours, or 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. Conclusions: Education programs in clinical psychology/psychotherapy may lag developments in AI/ML-enabled tools in mental healthcare. This survey of postgraduate clinical psychology and psychotherapy students raises questions about how curricula could be enhanced to better prepare clinical psychology/psychotherapy trainees to engage in constructive debate about ethical and evidence-based issues pertaining to AI/ML tools, and in guiding patients on the use of online mental health services and apps.