Challenges in Implementing Artificial Intelligence for Nursing Education: A Systematic Review

Abstract

Context: The use of artificial intelligence (AI) in health sciences education offers numerous benefits; however, a lack of awareness regarding the limitations of these tools may lead to financial and human losses. Objectives: Given that nurses constitute the largest segment of the healthcare team and their education is of paramount importance, this study explores the challenges associated with the use of AI in nursing education. Data Sources: A systematic review was conducted in 2025, utilizing databases such as ERIC, CINAHL, PubMed, Scopus, and Web of Science to search for relevant studies. The search employed appropriate keywords, with limitations set to articles published within the last five years and in English. Study Selection: After identifying relevant articles, their quality was assessed using suitable tools. This step ensured that only high-quality studies were included in the review, providing a reliable basis for analysis. Data Extraction: Various data were extracted, categorized, and analyzed. This comprehensive approach allowed for a thorough examination of the challenges associated with the use of AI in nursing education. Results: Out of 307 identified studies, 18 articles were reviewed. Based on the findings of this study, the challenges associated with the use of AI in nursing education were categorized into six main groups: Educational, technological, ethical, trust-building, human resource, and economic challenges. Conclusions: The challenges identified in this study were grouped into six primary categories, with the roots of these challenges linked to issues related to educational organizations, personnel capabilities, and the design of AI systems. Recognizing the challenges of using AI in nursing education can not only prevent unintended problems but also aid in preserving economic and human resources.

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