Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Redefining Faculty in the Age of Artificial Intelligence: Implications for Medical Education
0
Zitationen
1
Autoren
2025
Jahr
Abstract
IntroductionThe rapid embrace of digital technology, particularly artificial intelligence (AI), is leading to a paradigm shift for medical education. Peer-to-peer interactions, patient interactions, and human faculty have traditionally been the pillars of medical education. But increasingly, AI systems, from generative language models to intelligent tutoring platforms, assumed faculty-like roles, including lecturing, simulating clinical scenarios, providing feedback, and even advising students. Raising the issue of whether AI tutors should be recognised as “faculty” in medical education, this development does.The Development of AI Tutors From basic e-learning systems to advanced systems with competency- based testing, adaptive learning, and individualised feedback, AI-driven tools have evolved. Natural language processing-based models can generate case studies, quiz items, and explanations depending on the levels of proficiency of the learners.1 In parallel, AI-driven clinical case generators and virtual patient simulations provide realistic training environments that mirror the complexity of real healthcare.2 AI instructors now become interactive mentors as well as providingcontent. Anatomical and physiological simulations by AI provide individualised instruction previously only provided by human instructors, while conversational agents probe diagnostic reasoning.3,4 Such featuressuggest that AI’s role may go beyond the role of a simple “tool” and begin to approach that of a faculty member. How to cite this article:Raja D. Redefining Faculty in the Age of ArtificialIntelligence: Implications for Medical Education.Chettinad Health City Med J. 2025;14(3):1-3. DOI: https://doi.org/10.24321/2278.2044.202531
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.250 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.109 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.482 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.434 Zit.