Work

A Rapid Systematic Review of AI‐Generated and Human‐Generated Real‐Time Feedback in Nursing Education

Year: 2026

Type: review

Abstract: AIM: To explore the effects of AI-generated and human-generated real-time feedback on learning outcomes and experiences among undergraduate nursing students. BACKGROUND: Timely, high-quality feedback is crucial in nursing education to support clinical reasoning, skill development, and student engagement. While human-generated feedback remains central to pedagogy, emerging AI offers real-time feedback that complements traditional approaches. Understanding how both interventions influence student learning is critical to informing future curriculum and clinical practice. METHODS: We conducted a rapid systematic review of quantitative, qualitative, and mixed-methods studies across major databases from 2018 to 2025. Eligible studies involved undergraduate nursing students receiving real-time feedback generated from AI or humans. The Sample, Phenomenon of Interest, Design, Evaluation, and Research Type framework guided inclusion, and the Mixed Methods Appraisal Tool assessed methodological quality. FINDINGS: Nine studies were included. AI-generated tools supported timely, consistent, and individualised learning, contributing to student engagement, confidence, and skill development. Educator-mediated feedback, including technology-assisted and conventional human feedback approaches, supported student satisfaction, reflection, and emotional support. One study directly comparing AI-generated and human-generated feedback found that AI-generated feedback offered greater consistency and objectivity, whereas human-generated feedback provided contextual sensitivity and interpersonal connection; however, this comparison should not be generalised across the wider evidence base. DISCUSSION: AI offers opportunities to address scalability and access challenges in feedback delivery but does not fully replicate the relational aspects of educator feedback. CONCLUSION: AI-generated and human-generated feedback demonstrate distinct yet complementary strengths. A hybrid feedback model is proposed as a plausible approach that may optimise educational outcomes in nursing; however, the included studies have not directly evaluated it, and future empirical testing is needed before it can be recommended as the most effective approach. IMPLICATIONS FOR NURSING: Combining AI-generated and human-generated feedback demonstrates complementary strengths, and integrating them may optimise learning outcomes. IMPLICATIONS FOR HEALTH POLICY: Investment in digital education and faculty development is needed to support scalable feedback strategies globally. REPORTING METHOD: This systematic review adhered to relevant EQUATOR guidelines and followed the PRISMA guidelines (see Appendix 3).

Source: International Nursing Review

DOI: 10.1111/inr.70252

Authors: Rajarajeswari Rajasekaran, Jutharat Attawet, Pratibha Bhandari, Mercy Thomas, Yunjing Qiu

Institutions: Swinburne University of Technology, University of Technology Sydney

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Quality education

Open Access status: hybrid

Estimated APC paid: $3,400