A Rapid Systematic Review of AI‐Generated and Human‐Generated Real‐Time Feedback in Nursing Education
- 1Department of Nursing School of Health Sciences Swinburne University of Technology Hawthorn VIC Australia
- 2School of Nursing and Midwifery Faculty of Health University of Technology Sydney Sydney NSW Australia
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).
Related
- Ability of clinical assessment methods to differentiate levels of competence in undergraduate nursing education: a scoping review protocol.
- Effects of an AIGC‐Enhanced 3C Feedback Simulation on Critical Thinking in Nursing Education: A Mixed‐Methods Study
- Qualitative clinician feedback on a systematic urinary incontinence screening and patient education intervention in primary care
- Artificial intelligence generated virtual patients in health professions education: a scoping review.
- From Academic Integrity to Professional Formation in Generative AI-Supported Nursing Education: Integrative Review.
BibTeX
@article{rajasekaran2026rapid,
title = {{A Rapid Systematic Review of AI‐Generated and Human‐Generated Real‐Time Feedback in Nursing Education}},
author = {Rajarajeswari Rajasekaran and Jutharat Attawet and Pratibha Bhandari and Mercy Thomas and Yunjing Qiu},
journal = {International Nursing Review},
year = {2026},
month = oct,
doi = {10.1111/inr.70252},
url = {https://doi.org/10.1111/inr.70252},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Rapid Systematic Review of AI‐Generated and Human‐Generated Real‐Time Feedback in Nursing Education
AU - Rajarajeswari Rajasekaran
AU - Jutharat Attawet
AU - Pratibha Bhandari
AU - Mercy Thomas
AU - Yunjing Qiu
PY - 2026
DA - 2026/10/26
JO - International Nursing Review
DO - 10.1111/inr.70252
UR - https://doi.org/10.1111/inr.70252
AB - 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).
SN - 0020-8132
SN - 1466-7657
ER -