Quality matters: Artificial intelligence-based assessment of feedback quality predicts technical skill improvement
Artificial intelligence-driven assessment of feedback quality significantly correlates with technical skill improvement in a simulated procedural setting. By enhancing feedback quality, Teach1 taps into one of the strongest drivers of skill development in education, offering a scalable method to standardize feedback quality across surgical training and potentially improve patient outcomes.
Authors
- Ignacio Villagrán (ORCID: https://orcid.org/0000-0003-3130-8326)
- Cristian Jarry Trujillo
- Philip S. Barie (ORCID: https://orcid.org/0000-0002-5848-527X)
- Francisca Rammsy (ORCID: https://orcid.org/0000-0003-3601-8985)
- Advaith Bongu (ORCID: https://orcid.org/0009-0009-5339-3139)
- Gabriel Escalona (ORCID: https://orcid.org/0000-0001-8733-3614)
- Julián Varas (ORCID: https://orcid.org/0000-0002-5828-9623)
- Maria Felice Brizzi (ORCID: https://orcid.org/0000-0003-0944-6992)
- Divya Kewalramani (ORCID: https://orcid.org/0009-0004-9125-0909)
- Mayur Narayan
- Diego Sanhueza Roman
- Sofía Abedrapo Lagos
Institutions
- Rutgers, The State University of New Jersey (US)
- Twitter (United States) (US)
- Pontificia Universidad Católica de Chile (CL)
- Cornell University (US)
- Johnson University (US)
Publication Details
- Journal
- Surgery
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1016/j.surg.2025.109625
- Citations
- 1
- Primary Topic
- Engineering Education and Curriculum Development
- Type
- article
- Field-Weighted Citation Impact
- 1.14