Training future psychotherapists with AI-enabled virtual patients: A randomized study

Wed, 15 Jul 2026 00:00:00 +0000

Preparing psychotherapy trainees for the complexity of real clinical encounters is a persistent challenge. AI-enabled virtual patients – simulated patients with whom trainees can conduct conversations and receive automated feedback – promise hands-on practice without risks for real patients. In this preregistered study led by Julia Cecil, we tested whether such simulations improve training outcomes.

What we did

87 psychology students and psychotherapy trainees from Germany and the UK were randomly assigned to either four online sessions with virtual patients (with automated feedback) or four sessions with pre-recorded role-play videos. Each session focused on one disorder – depression, panic disorder, generalized anxiety disorder, or adjustment disorder. Before and after the training, we measured perceived psychotherapeutic competence, clinical self-efficacy, knowledge, and insecurity.

What we found

Virtual patient training improved perceived competence and self-efficacy and reduced insecurity – but these effects did not exceed those of the video training.
Neither training increased knowledge about mental disorders.
More clinical experience was associated with higher competence and lower insecurity, but did not change how much participants gained from the training.
Insecurity decreased earlier with the video training than with the virtual patients.

Mean (a) competence, (b) self-efficacy, (c) knowledge, and (d) insecurity before and after the training. Figure 3 from Cecil et al. (2026), BMC Medical Education, licensed under CC BY 4.0.

What this means

AI-enabled virtual patients are a scalable and engaging complement to psychotherapy training, not (yet) a superior alternative. Good pre-briefings and more authentic virtual patients could increase their benefits.

Read the paper

Cecil, J., Kokje, E., Kleine, A.-K., Schaffernak, I., Vogel, L., Angerer, S., Lermer, E., & Gaube, S. (2026). The effect of AI-enabled virtual patient simulation on training outcomes and insecurities in psychotherapy education. BMC Medical Education, 26, 1156. https://doi.org/10.1186/s12909-026-09917-x

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

education on Anne-Kathrin Kleine

Training future psychotherapists with AI-enabled virtual patients: A randomized 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