Use of Artificial Intelligence (AI) in Nursing Education and Its Impact: A Systematic Review

Abstract

Abstract\r\nBackground: With the rapid integration of Artificial Intelligence (AI) in healthcare systems globally, nursing education has become a key area of exploration for AI-driven technologies such as virtual simulations, intelligent tutoring systems, adaptive learning platforms and chatbots. These innovations have the potential to transform the way nursing students acquire theoretical knowledge, clinical reasoning, decision-making ability, communication skills and psychomotor competencies in safe and controlled learning environments. However, there is limited synthesis of evidence regarding the measurable effectiveness of AI-based educational interventions in nursing education.\r\nObjective: This systematic review aimed to critically evaluate empirical studies investigating the use of AI technologies in nursing education and their impact on student learning outcomes across cognitive, psychomotor and affective domains.\r\nMethod: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in PubMed, CINAHL, Scopus and Google Scholar for studies published from January 2018 to March 2025. Studies were included if they examined AI-based interventions among nursing students and reported measurable learning outcomes. After screening 82 articles, 12 studies met the inclusion criteria and were included for data extraction and narrative synthesis. Quality assessment was conducted using Joanna Briggs Institute Critical Appraisal tools.\r\nResults: AI interventions identified in the review included virtual reality simulations, chatbot-based learning systems, intelligent tutoring tools, AI-supported clinical decision-making simulations and AI-enhanced debriefing systems. Across the included studies, AI demonstrated moderate-to-large positive effects on knowledge retention, clinical reasoning, learner engagement and procedural confidence. Improvements were especially notable in critical thinking, self-directed learning and confidence in performing nursing procedures. However, challenges such as limited faculty preparedness, technical barriers, infrastructure limitations, ethical concerns, data privacy issues and lack of long-term outcome evaluation were identified.\r\nConclusion: The evidence suggests that AI has significant potential to enhance nursing education across multiple learning domains. AI-based learning tools can support personalized learning, repeated practice, timely feedback and safe simulation of complex clinical situations. However, effective integration of AI in nursing education requires robust infrastructure, faculty development, ethical governance and curriculum-level planning. Future research should focus on longitudinal studies, multicentric trials and evaluation of AI’s impact on sustained clinical competence and ethical decision-making.\r\n