Importance of AI Chatbot in Nursing Education: A Systematic Review

Artificial intelligence (AI) chatbots and large language models (LLMs) have rapidly gained attention in nursing education for their potential to enhance simulation-based learning, individualized tutoring, and curriculum efficiency. Despite growing use, the evidence on their educational effectiveness, implementation, and ethical implications remains fragmented. Objective: To systematically review the literature on AI chatbot applications in nursing education, synthesizing their pedagogical benefits, implementation challenges, ethical considerations, and recommendations for practice. Methods: A systematic literature synthesis was performed in alignment with PRISMA-ScR reporting standards. Databases including PubMed, Scopus, CINAHL, and IEEE Xplore were searched for empirical and review studies between 2021 and 2025. Eligible studies involved AI chatbots or LLMs explicitly used within nursing education. Two reviewers independently screened and extracted data. Quality appraisal was conducted using mixed-evidence evaluation criteria covering experimental, qualitative, and review studies. Results: Thirty-three eligible studies were included, encompassing systematic reviews, quasi-experimental trials, qualitative studies, and narrative syntheses. Evidence consistently demonstrates that AI chatbots enhance clinical reasoning, personalized learning, scenario generation, and student engagement. Faculty and student perceptions were largely positive, though concerns emerged regarding ethical implications, accuracy (hallucination risks), privacy, and overreliance on AI tools. Implementation frameworks such as ALIGN and dual-phase evaluation models emphasize faculty training, ethical oversight, and structured governance. Limitations across studies included methodological heterogeneity, small sample sizes, and limited long-term follow-up. Conclusion: AI chatbots show promising potential as innovative educational tools in nursing. They improve engagement and learning outcomes but pose ethical and accuracy-related risks. Responsible integration demands robust governance, AI literacy, and human-centered pedagogy. Further randomized controlled trials and standardized reporting are essential for high-impact, reproducible evidence.