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Using AI-Based Virtual Simulated Patients for Training in Psychopathological Interviewing: Cross-Sectional

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Summary

Generative artificial intelligence (GAI) virtual simulated patients (VSPs) are well-received by psychology students for clinical interviewing skills training. Student gender, GAI temperature, and connection stability influenced perceptions, though objective performance did not significantly improve.

Keywords:
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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Psychology Training

Background:

  • Generative artificial intelligence (GAI) virtual simulated patients (VSPs) show potential for training clinical interviewing skills.
  • Little is known about how system and user variables affect student perceptions of GAI-driven VSP interactions.

Purpose of the Study:

  • To investigate psychology students' perceptions of GAI-driven VSPs.
  • To examine the influence of demographic factors, system parameters, and interaction characteristics on these perceptions.

Main Methods:

  • 1832 interactions between 156 psychology students and 13 GAI VSPs with varied temperature settings (0.1, 0.5, 0.9).
  • Data collected included student demographics, interview length, connectivity failures, VSP used, and temperature setting.
  • Student ratings, comments, and perceived diagnostic ability were recorded; sentiment analysis assessed clinical realism.

Main Results:

  • Female students rated VSPs higher (9.25/10) than male students (8.94/10).
  • Higher ratings correlated with fewer connectivity failures and higher GAI temperature settings (0.9 vs. 0.1).
  • 94% of students perceived improved diagnostic ability, but objective scores did not significantly differ from a non-VSP cohort.

Conclusions:

  • GAI-driven VSPs are positively received by psychology students, with gender and system variables influencing evaluations.
  • While students felt training improved diagnostic skills, objective outcomes showed no significant gains.
  • Further research is needed due to limitations in randomization and generalization.