2025, Vol. 7, Issue 2, Part B

author(s)
Sharanabasappa Hipparagi

abstract
Advances in artificial intelligence (AI) and machine learning (ML) are increasingly transforming healthcare education, including the field of psychiatric nursing. Traditional psychiatric nursing education faces persistent challenges such as limited clinical exposure, patient safety concerns, and difficulties in teaching complex interpersonal, emotional, and cognitive skills. AI- and ML-enhanced simulation technologies offer innovative solutions by providing immersive, adaptive, and learner-centered environments that support experiential learning and the development of clinical competence. This review synthesizes existing evidence on the application of AI- and ML-enhanced simulation in psychiatric nursing education, with a focus on educational outcomes, technological approaches, ethical considerations, and implementation challenges. A systematic narrative review was conducted following PRISMA guidelines. Major databases including PubMed, Scopus, Web of Science, CINAHL, PsycINFO, and IEEE Xplore were searched for studies published between 2010 and 2024. Quantitative, qualitative, and mixed-methods studies examining AI- or ML-based simulation in psychiatric or mental health nursing education were included. Data were extracted and thematically synthesized, and methodological quality was appraised using standardized tools such as the MMAT, JBI checklists, and MERSQI. A total of 20 studies met the inclusion criteria. Findings indicate that AI-enhanced simulation modalities such as virtual patients, virtual and augmented reality, intelligent tutoring systems, and conversational agents significantly improve clinical reasoning, therapeutic communication, learner confidence, and preparedness for psychiatric practice. Adaptive and personalized learning environments enhanced engagement and supported individualized competency development. However, challenges related to ethical concerns, algorithmic bias, data privacy, faculty preparedness, and infrastructural limitations were commonly reported. While short-term educational benefits were well documented, evidence on long-term clinical outcomes remains limited. Overall, AI- and ML-enhanced simulation represents a promising and transformative approach in psychiatric nursing education. Its successful integration requires careful ethical governance, faculty capacity building, and preservation of humanistic care values. Future research should prioritize longitudinal evaluation, standardized outcome measures, and culturally inclusive AI design to support sustainable and responsible implementation in nursing education.