JMIR Publications

ABSTRACT

Background:

Clinical reasoning is crucial in psychology education, yet traditional training methods provide limited practical experience. Virtual patients (VPs), enhanced by generative artificial intelligence (GAI), may effectively bridge this gap, offering realistic simulations that promote diagnostic and reasoning skills in a controlled environment

Objective:

To evaluate the impact of GAI-powered conversational virtual patients on active learning, student satisfaction, participation levels, and overall educational experience in an undergraduate psychopathology course.

Methods:

The study involved 160 second-year psychology undergraduates at Miguel Hernández University, who engaged in structured text-based interviews with virtual patients generated by ChatGPT (gpt-4o model). Each student participated in one to six sessions, resulting in 1,832 recorded interactions. AI temperature settings (0.1, 0.5, 0.9) were systematically varied to examine their effect on interactions and perceptions. Sentiment analysis was conducted using Python's "pysentimiento" library, and quantitative data were analyzed with R software.

Results:

Participants rated the platform highly, with median ratings close to 10 across different conditions. Statistical analysis revealed no significant correlation between age (p = 0.42) or number of questions asked (p = 0.42) and user ratings. A moderate negative correlation was found between AI errors and ratings (r = –0.31, p < 0.001). Temperature settings significantly influenced ratings (Kruskal-Wallis test, p = 0.031), with higher ratings at the 0.9 temperature compared to 0.1 (Dunn's test, p = 0.037). Sentiment analysis showed predominantly negative sentiment in AI responses (median negativity = 0.8903), reflecting clinical realism.

Conclusions:

GAI-powered conversational VPs significantly enhance clinical training in psychopatology skills, providing realistic, engaging simulations that improve student satisfaction and clinical reasoning skills. Optimizing AI temperature settings can further enhance educational effectiveness, highlighting the value of carefully tailored simulation parameters.