Educational Strategies for Enhancing AI Literacy among Nursing Students: A Systematic Review

Background: Artificial intelligence (AI) is rapidly entering nursing education and practice, yet AI literacy among nursing students remains uneven, with gaps in foundational knowledge, ethical reasoning, and applied clinical judgment. Objective: To synthesize educational strategies that enhance AI literacy among nursing students and identify outcome patterns, barriers, and implementation enablers to inform curriculum, assessment, and policy. Methods: A systematic literature review (2015-2025) was conducted across PubMed, Scopus, ScienceDirect, CINAHL, and ERIC. English-language, peer-reviewed studies focusing on pre- or post-licensure nursing students and reporting AI-related educational outcomes were included. Two reviewers independently screened records, extracted data into a structured matrix, and appraised quality using CASP (qualitative) and JBI (quantitative/mixed-methods) tools. Narrative and thematic synthesis was used to integrate findings. Results: Of 364 records identified, 47 duplicates were removed; 317 titles/abstracts were screened, 63 full texts were sought (60 assessed), and 28 studies met inclusion. Four pedagogical themes emerged: (1) simulation-based learning using AI-enabled or GenAI-supported scenarios; (2) online/blended modules for scalable foundational concepts; (3) problem-/case-based learning (PBL/CBL) to situate AI within clinical reasoning and communication; and (4) cross-disciplinary/policy approaches aligning competencies, assessment, and governance. Across diverse settings, interventions improved knowledge, confidence/readiness, and higher-order thinking. Transfer to clinical judgment was strongest for PBL/CBL and simulation with structured debriefs. Recurrent barriers included limited faculty readiness, student anxiety about AI's impact on nursing identity, infrastructural constraints (devices/connectivity), and uneven treatment of ethics, bias, and accountability. Studies rarely measured longitudinal behavior change or patient-centered outcomes. Conclusions: AI literacy is achievable at scale when foundations are delivered via blended learning, transfer is secured through PBL/CBL, and safe practice is consolidated through simulation and guided debriefing-underpinned by robust assessment, faculty development, equitable infrastructure, and clear policy for human-in-the-loop accountability. Future research should adopt standardized measures and longitudinal designs to link classroom gains to clinical behaviors and patient outcomes.