Blending human and AI-powered feedback models in medical education: A practical overview
Effective feedback plays a pivotal role in medical education, bridging the gap between current and desired learner performance. This short communication outlines common types and models of feedback in clinical teaching and explores how artificial intelligence (AI) tools, such as ChatGPT, can complement traditional methods. While AI can offer immediate, data-driven insights, the irreplaceable human element brings contextual awareness and emotional intelligence to feedback processes. We present a practical categorization of feedback types and a comparative overview of ten well-established models. By integrating human expertise with AI-supported systems, educators can enhance formative assessment and foster autonomous, reflective learning. Practical implications are discussed for implementing feedback models in both in-person and digital learning environments.
Authors
- Masomeh Kalantarion (ORCID: https://orcid.org/0000-0003-4778-3973)
- Mahdieh Zangiabadizadeh
- Saeed Latifi (ORCID: https://orcid.org/0000-0003-0420-7474)
- Ehsan Toofaninejad (ORCID: https://orcid.org/0000-0001-6440-0992)
Institutions
- Kharazmi University (IR)
- Islamic Azad University Kerman (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- Research and Development in Medical Education
- Published
- 2025-10-19
- DOI
- https://doi.org/10.34172/rdme.025.33353
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shahid Beheshti University of Medical Sciences