Training behavioral health students in the use of artificial intelligence (AI): a framework for graduate education

Abstract

Despite widespread adoption of artificial intelligence (AI), behavioral health practitioners often feel ill-prepared to use the technologies in their clinical services. While, graduate school has been identified as an optimal time to introduce AI-related topics, to date, literature on the integration of AI-focused education into graduate programming remains limited. The current manuscript aimed to provide a broad framework for both didactic and experiential AI-focused education integration into behavioral health graduate training. The manuscript is a qualitative narrative synthesis of key literature to provide guidance for behavioral health graduate training programs. While literature suggested fragmentation of information, as well as a lack of universal guidance, it also highlighted several findings relevant to the integration of AI-focused education into graduate programming. First, an author-initiated thematic consolidation identified twenty-two AI-related competencies recommended for behavioral health practitioner education. Second, literature indicated multiple optimal time points and methods of implementing AI-focused education throughout a student’s training activities. Finally, literature noted a range of methodologies for outcome monitoring. Manuscript content is believed to assist graduate-level training programs in designing AI-focused educational curriculum and activities. While AI continues to be rapidly and widely integrated into behavioral healthcare services, education on ethical, legal, evidence-informed and safe practices remains scarce, fragmented and still developing. The current discussion provided a broad framework for behavioral health graduate training programs to implement AI-focused didactic and experiential training to foster greater preparation for students as they enter the healthcare field.