en Nursing Review Articels <div style="text-align: justify;"><span style="line-height:2;"><span style="font-size:14px;"><span style="font-family:Times New Roman;"><span style="background:white"><span style="color:#2980b9;"><strong>Background:</strong></span><span style="color:#0f1115">&nbsp;Preventable medical errors are a major concern in clinical care, highlighting the importance of effective nursing education for clinical decision-making. Recently, AI-based educational strategies have emerged, but their actual impact on nursing students has not been systematically assessed.</span><br> <span style="color:#2980b9;"><strong>Objective:</strong></span><span style="color:#0f1115">&nbsp;This systematic review aimed to synthesize the available evidence on the impact of AI‑based educational programs on clinical decision‑making and medical errors among nursing students. </span></span><br> <span style="background:white"><span style="color:#2980b9;"><strong>Methods:</strong></span><span style="color:#0f1115"> This systematic review was conducted according to PRISMA 2020. PubMed, Scopus, Web of Science, Cochrane CENTRAL, and Embase were searched for relevant studies (2019&ndash;2024). Eligible studies were randomized controlled trials (RCTs) and quasi-experimental designs comparing AI-based interventions with traditional education in undergraduate nursing students. Study selection, data extraction, and quality appraisal (RoB-2 and ROBINS-I) were performed independently. </span><span lang="EN-GB" style="color:#0f1115">D</span><span style="color:#0f1115">ue to substantial heterogeneity among the included studies, a narrative synthesis was performed.<strong> </strong></span></span><br> <span style="color:#2980b9;"><strong>Results:</strong></span>&nbsp;The initial search identified 1,487 records, of which 16 studies involving approximately 1,590 nursing students met the inclusion criteria. AI‑based interventions, mainly virtual patient simulations and adaptive learning systems, significantly enhanced clinical decision‑making skills in all included studies. Moreover, most studies assessing medical errors (9 of 10) reported notable reductions in medication miscalculations and diagnostic inaccuracies. Overall, the methodological quality of the included studies was rated as moderate to good. <span style="background:white"><span style="color:#0f1115"></span></span><br> <span style="background:white"><span style="color:#2980b9;"><strong>Conclusion:</strong></span><span style="color:#0f1115">&nbsp;<span style="background:white">AI-based education shows strong potential to improve clinical judgment and promote patient safety in nursing students. Careful, context-sensitive integration into nursing curricula is recommended, with future research needed to address methodological heterogeneity and long-term effectiveness.</span></span></span></span></span></span><br> &nbsp;</div> Artificial Intelligence, Nursing Education, Clinical Decision-Making, Medical Errors, Patient Safety, Systematic Review, Narrative Synthesis. 85 95 http://nmcjournal.zums.ac.ir/browse.php?a_code=A-10-1072-1&slc_lang=en&sid=1 sedigh mohamadi parastari137464@gmail.com 100319475328460014796 100319475328460014796 Yes Department of Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran. Amir Jalali Jalali_amir@yahoo.com 100319475328460014797 100319475328460014797 No Substance Abuse Prevention Research Center, Research Institute for Health, Kermanshah University of Medical Sciences, Kermanshah, Iran