Preliminary Evaluation of a Stage-Structured Retrieval-Augmented Systematic Review Learning Agent for Undergraduate Nursing Students: A Proof-of-Concept Study

Background: Generative artificial intelligence (AI) may support systematic review learning, but general-purpose chatbots and workflow tools do not explicitly teach methodological reasoning. Purpose: To develop a stage-structured retrieval-augmented generation learning agent and assess its perceived pedagogical fit and technical performance. Methods: The tool combined planning, query rewriting, iterative retrieval, and context-sufficiency checking across 6 stages: topic selection, review question framing, search strategy, screening and management, critical appraisal and data extraction, and synthesis and reporting. Six nurse educators and 4 undergraduate nursing students completed author-developed questionnaires assessing perceived pedagogical fit and AI performance. Results: The tool generated stage-aligned guidance across the review process. Participants reported moderate perceived pedagogical fit ( M = 3.90, standard deviation = 0.45) and high perceived AI performance ( M = 4.00, standard deviation = 0.25); personalization required improvement. Conclusions: The tool was feasible as an instructional scaffold, but findings reflect perceptions from a small sample. Larger studies should assess objective learning outcomes, methodological accuracy, and responsible AI use.