Bytes versus brains
A comparative study of AI-generated feedback and human tutor feedback in medical education
Bibliographic Data
| ID | 21447732 |
|---|---|
| Authors | Majid Ali (0000-0002-3690-2183, College of Medicine Sulaiman Al-Rajhi University Al-Bukayriyah, corresponding author), Ihab Harbieh (College of Medicine Sulaiman Al-Rajhi University Al-Bukayriyah), Khawaja Husnain Haider (0000-0002-7907-4808, College of Medicine Sulaiman Al-Rajhi University Al-Bukayriyah) |
| Year | 2026 |
| Volume | 48 |
| Issue | 1 |
| Pages | 131-141 |
| Publication date | 2026-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Teacher (JOURNAL) |
| Journal identifiers | ISSN: 0142-159X • E-ISSN: 1466-187X |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/0142159x.2025.2519639 |
| PMID | 40528415 |
| OpenAlex | W4411432023 |
| Language | EN |
| Citations received | 2 |
| References cited | 28 |
INTRODUCTION: Timely, high-quality feedback is vital in medical education but increasingly difficult due to rising student numbers and limited faculty. Artificial intelligence (AI) tools offer scalable solutions, yet limited research compares their effectiveness with traditional tutor feedback. This study examined the comparative effectiveness of AI-generated feedback versus human tutor feedback within the medical curriculum. METHODS: Second-year medical students (n = 108) received two sets of feedback on a written assignment, one from their tutor and one unedited response from ChatGPT. Students assessed each feedback using a structured online questionnaire focused on key feedback quality criteria. RESULTS: Eighty-five students (79%) completed the evaluation. Tutor feedback was rated significantly higher in clarity and understandability (p < 0.001), relevance (p < 0.001), actionability (p = 0.009), comprehensiveness (p = 0.001), accuracy and reliability (p = 0.003), and overall usefulness (p < 0.001). However, 62.3% of students indicated that both pieces of feedback complemented each other. Open-ended responses aligned with these quantitative findings. . CONCLUSION: Human tutors currently provide superior feedback in terms of clarity, relevance, and accuracy. Nonetheless, AI-generated feedback shows promise as a complementary tool. A hybrid feedback model integrating AI and human input could enhance the scalability and richness of feedback in medical education
CLARITY · Curriculum · Formative assessment · Mathematics education · Medical education · Pedagogy · Peer feedback · Quality (philosophy) · TUTOR · Artificial Intelligence in Healthcare and Education · Computer Science · Innovations in Medical Education · Medicine · Psychology · Student Assessment and Feedback
Comparing the quality of human and ChatGPT feedback of students’ writing
Critical Inquiry in a Text-Based Environment
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher–AI Complementarity
Workplace-based assessment as an educational tool
Exploring the impact of artificial intelligence on teaching and learning in higher education
Designing educational technologies in the age of AI
Hybrid Intelligence
Student Perceptions of AI-Generated Avatars in Teaching Business Ethics
Rethinking models of feedback for learning
Those Who Understand
Systematic review of research on artificial intelligence applications in higher education – where are the educators
Qualitative inquiry and research design
The Power of Feedback
Situated Learning
| Citations per year | 2 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |