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Preliminary Evaluation of a Stage-Structured Retrieval-Augmented Systematic Review Learning Agent for Undergraduate
Yingchun Zeng1, Liting Fang, Zixuan Wang
1Author Affiliations: Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore (Dr Zeng, Dr Jiang, Dr Siah, Dr Klainin-Yobas, Dr Lau); School of Computers and Computing Sciences, Hangzhou City University, Hangzhou, China (Ms Fang); and College of Computer Science and Technology, Zhejiang University, Hangzhou, China (Mr Wang).
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.
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