Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review

Background: Clinical reasoning (CR) is a core competency in undergraduate nursing education, directly influencing patient safety and quality of care. Artificial intelligence (AI)–enabled educational tools are increasingly integrated into nursing curricula; however, their effectiveness in enhancing CR remains uncertain due to heterogeneous technologies, outcome measures, and study designs. Objective: To systematically evaluate the effectiveness of AI-enabled educational tools in improving clinical reasoning among undergraduate nursing students compared with traditional teaching approaches or no AI intervention. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. Five databases were searched for studies published between January 2022 and January 15, 2026. Eligible studies involved undergraduate nursing students and reported explicitly operationalized outcomes related to clinical reasoning, clinical judgment, or clinical decision-making. After removal of 332 duplicates, 1,015 records were screened; 33 full texts were assessed, and 9 studies met inclusion criteria. Methodological quality was appraised using Joanna Briggs Institute (JBI) checklists. Results: Nine studies (n = 9) were included, comprising two randomized controlled trials, quasi-experimental designs, and qualitative studies. Interventions included AI-enhanced simulations, rule-based educational chatbots, generative AI/LLM tools, and AI-integrated tutoring systems. Improvements in self-reported clinical reasoning were observed in some studies, whereas others reported null or mixed findings. Performance-based or proxy measures yielded heterogeneous results, and qualitative studies highlighted perceived benefits in information organization and confidence alongside concerns regarding dependency and reduced critical engagement. Risk-of-bias assessment revealed methodological limitations, particularly in non-randomized designs. Conclusion: Current evidence suggests preliminary and context-dependent educational potential for AI-enabled tools in supporting aspects of clinical reasoning. However, findings are heterogeneous, frequently based on self-reported or proxy measures, and limited by methodological constraints. AI should therefore be considered a complementary pedagogical resource rather than a substitute for supervised clinical mentorship. Further rigorously designed studies using standardized performance-based CR measures are needed.