Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Designing AI-resilient admissions interviews for health professions training in the age of generative AI
0
Zitationen
3
Autoren
2026
Jahr
Abstract
Virtual interviews have now become standard practice in health professions training admissions following the COVID-19 pandemic, but the advent of generative AI technologies has raised concerns about the fairness and integrity of such practices. Admissions programs are responding with AI detection software, increased proctoring, and outright bans, all of which are difficult to enforce. A recent randomized controlled trial by Eva and colleagues examining Generative AI tool use among applicants to a medical school during virtual Multiple Mini‑Interviews (MMIs) suggests a different solution: good interview structure may be more resistant to AI advantage, and minor modifications can limit AI use without compromising reliability,authenticity or acceptability. Reframing generative AI as a design problem rather than a detection problem may also help align integrity, equity, and learning values.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.707 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.613 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.159 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.875 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.