Do mental health practitioners want to learn about and use AI? An international mixed-methods study

Wed, 16 Apr 2025 00:00:00 +0000

The demand for mental healthcare far exceeds the available therapeutic resources, and AI-enabled technologies may help to support and deliver care. But how familiar are practitioners with these technologies, and who intends to learn about and use them? In this preregistered study led by Julia Cecil, we looked at four application areas: diagnostics, treatment, feedback, and practice management.

What we did

We surveyed 392 mental health practitioners from Germany (n = 236) and the United States (n = 156) – psychotherapists (in training), psychiatrists, and clinical psychologists. We analyzed open answers about their understanding of AI with a deductive thematic approach, and we used structural equation modeling to relate practitioners’ characteristics (e.g., AI readiness, AI anxiety, technology self-efficacy, affinity for technology, and professional identification) to their learning and use intentions.

What we found

There is a substantial familiarity gap: 45% had never heard of AI-enabled technologies in psychotherapy or psychiatry, only about 10% had actively looked into the topic, and 93% had never used such tools in practice.
Learning intentions were higher than use intentions. Both were highest for practice management tools and lowest for diagnostic tools – practitioners were more hesitant about patient-centred than about therapist-centred applications.
Across all four application areas, learning intention, ethical knowledge, and affinity for technology interaction were relevant predictors.
Psychiatrists reported higher intentions than the other professional groups, and practitioners in Germany reported lower learning intentions than those in the US.

(a) Learning intentions and (b) use intentions across the four application areas. Figure 3 from Cecil et al. (2025), BMC Health Services Research, licensed under CC BY 4.0.

What this means

Before AI can be implemented in mental healthcare, practitioners need opportunities to learn about it – including its ethical implications. Training programs that build knowledge and address concerns are a promising first step toward responsible adoption.

Read the paper

Cecil, J., Kleine, A.-K., Lermer, E., & Gaube, S. (2025). Mental health practitioners’ perceptions and adoption intentions of AI-enabled technologies: An international mixed-methods study. BMC Health Services Research, 25, 556. https://doi.org/10.1186/s12913-025-12715-8

What drives students' daily use of AI chatbots? Within- and between-person effects

Tue, 03 Dec 2024 00:00:00 +0000

AI chatbots such as ChatGPT have become part of everyday student life. But why do some students use them intensively and others hardly at all – and why do the same students use them more on some days than on others? Most research on technology acceptance relies on one-time surveys and therefore cannot separate stable differences between people from fluctuations within people.

What we did

Building on the Technology Acceptance Model (TAM) and its extension TAM3, we conducted two daily diary studies over five days with university students (Study 1: N = 72; Study 2: N = 153). Using multilevel structural equation modeling, we separated between-person from within-person relationships. Study 2 additionally examined facilitating conditions and subjective norm as predictors.

What we found

AI chatbot anxiety was associated with perceived ease of use and usefulness, which serially mediated its link with usage intensity (Study 1).
Between persons: in both studies, students who on average found chatbots easier to use and more useful used them more intensively.
Within persons: in Study 1, the day-to-day link between ease of use and usage intensity was mediated by perceived usefulness. Study 2 did not replicate this daily mediation – and day-to-day variability in ease of use and usefulness was much lower in Study 2 than in Study 1.

What this means

To promote the meaningful use of AI chatbots in higher education, user-friendly interfaces, reducing AI-related anxiety, robust technical support, and peer influence all matter. Methodologically, the study shows why it pays off to consider both stable individual differences and daily dynamics when studying how people adopt AI.

Read the paper

Kleine, A.-K., Schaffernak, I., & Lermer, E. (2025). Exploring predictors of AI chatbot usage intensity among students: Within- and between-person relationships based on the technology acceptance model. Computers in Human Behavior: Artificial Humans, 3, 100113. https://doi.org/10.1016/j.chbah.2024.100113

technology acceptance on Anne-Kathrin Kleine

Do mental health practitioners want to learn about and use AI? An international mixed-methods study

What we did

What we found

What this means

Read the paper

What drives students' daily use of AI chatbots? Within- and between-person effects

What we did

What we found

What this means

Read the paper