AI-Generated Feedback for Nursing Documentation and Clinical Reasoning Training: A Scoping Review of Applications and Validation Approaches
Abstract
Objective: This scoping review aimed to map the application characteristics of AI-generated feedback in nursing documentation and clinical reasoning training and to synthesize the validation approaches used to establish trustworthiness.
Material and Methods: Guided by the Arksey and O’Malley framework, a structured search was conducted across five databases for peer-reviewed studies published between 2021 and 2025. Fourteen studies met the eligibility criteria. Data were synthesized descriptively to characterize AI application features and validation practices.
Results: The included studies utilized diverse AI systems, including LLM-based platforms, chatbot tutors, retrieval-augmented generation (RAG) systems, automated scoring engines, and mixed-reality applications, delivering both real-time and post-task feedback across varied nursing learning contexts. Validation approaches were highly heterogeneous, revealing substantial gaps in the evidence of trustworthiness. While many studies demonstrated strong evidence in expert review, guideline/rubric alignment, and reliability testing, evidence for robustness across cases and safety safeguards remained limited.
Conclusion: The findings highlight a critical validation gap, in which technological adoption is advancing faster than the evidence supporting technical reliability, educational validity, and clinical safety trustworthiness. Future research should prioritize standardized validation frameworks that explicitly address safety-critical metrics to ensure the responsible implementation of AI-generated feedback in nursing education.
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