Comparison of Feedback from ChatGPT and Human Professors in Higher Education: A Systematic Review

Abstract

Objectives: To compare the feedback provided by human professors and ChatGPT on university students’ work and to report on students’ perceptions of both types of feedback. Materials and Methods: A systematic review was conducted following PRISMA 2020 guidelines. Databases searched included Web of Science, SCOPUS, EBSCO, ACM Digital Library, and IEEE Xplore, with additional gray literature sources, until February 2025. Inclusion criteria were cross-sectional studies evaluating university students’ work, comparing feedback from ChatGPT with human professors. Data extraction was performed using a standardized form, and risk of bias was assessed with the Joanna Briggs Institute Critical Appraisal Tool. A narrative synthesis of the results was made. PROSPERO registration number: CRD42024566691. Results: The review included 8 studies with 461 students. ChatGPT feedback was detailed and rapid, while human feedback was valued for its personalization and emotional support. The differences between ChatGPT and human feedback were insignificant, as both were similar. Students appreciated the detailed and immediate nature of ChatGPT feedback but noted its lack of emotional nuance and context-specific guidance. Human feedback was preferred for addressing individual learning needs and providing affective support. A combination of both types of feedback can maximize benefits. Conclusions: ChatGPT can assist human teachers by providing detailed and timely feedback to university students. However, human supervision is essential to ensure feedback is nuanced and contextually appropriate. A hybrid approach can optimize the learning experience in higher education. Further research is necessary to explore AI applications in educational settings and understand their impact on learning outcomes.

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