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Background and purpose <p>This qualitative case study describes the development, implementation, and analysis of an artificial intelligence (AI)-driven patient simulation for a case-based interprofessional education (IPE) forum. The project aimed to create a responsive, authentic AI patient character to simulate a complex clinical encounter and assess how students from nursing, dietetics, nurse practitioner, and counseling psychology programs interacted with the AI-simulated patient to collaboratively develop a person-centered care plan. The AI client, a 37-year-old woman preparing for bariatric surgery, was iteratively designed to reflect complex health and psychosocial issues, with prompts refined for realism and consistency.</p> Outcomes <p>Six interprofessional student groups participated, conducting simulated patient interviews and developing care plans. Reflexive thematic analysis of student-created ChatGPT transcripts revealed most groups engaged deeply, asking detailed follow-up questions integrating physical, nutritional, and psychological care. The AI enabled exploration of diverse clinical paths, though some groups demonstrated limited interprofessional integration. The AI maintained a consistent patient persona and adapted to timeline discrepancies, but occasional hallucinations and minor inconsistencies were noted.</p> Discussion and conclusion <p>This case study demonstrates that AI-driven simulations provide realistic, interactive experiences that foster collaboration and clinical reasoning. Careful prompt development and thoughtful facilitation supported authenticity. The results encourage further exploration of scalable AI-driven IPE cases and their effect on student learning. This case offers a practical framework for integrating AI into interprofessional education.</p>

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A qualitative study in evaluating an AI-driven patient simulation in interprofessional education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月1日

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  • Maritza Dominguez,
  • Chad Lairamore,
  • Mike Casey,
  • Brandy Pate,
  • Christina E. Jeffrey

摘要

Background and purpose

This qualitative case study describes the development, implementation, and analysis of an artificial intelligence (AI)-driven patient simulation for a case-based interprofessional education (IPE) forum. The project aimed to create a responsive, authentic AI patient character to simulate a complex clinical encounter and assess how students from nursing, dietetics, nurse practitioner, and counseling psychology programs interacted with the AI-simulated patient to collaboratively develop a person-centered care plan. The AI client, a 37-year-old woman preparing for bariatric surgery, was iteratively designed to reflect complex health and psychosocial issues, with prompts refined for realism and consistency.

Outcomes

Six interprofessional student groups participated, conducting simulated patient interviews and developing care plans. Reflexive thematic analysis of student-created ChatGPT transcripts revealed most groups engaged deeply, asking detailed follow-up questions integrating physical, nutritional, and psychological care. The AI enabled exploration of diverse clinical paths, though some groups demonstrated limited interprofessional integration. The AI maintained a consistent patient persona and adapted to timeline discrepancies, but occasional hallucinations and minor inconsistencies were noted.

Discussion and conclusion

This case study demonstrates that AI-driven simulations provide realistic, interactive experiences that foster collaboration and clinical reasoning. Careful prompt development and thoughtful facilitation supported authenticity. The results encourage further exploration of scalable AI-driven IPE cases and their effect on student learning. This case offers a practical framework for integrating AI into interprofessional education.