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Using Artificial Intelligence/Machine Learning to identify assessment needs and to assist faculty decisions

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Resumo:Medical education is a discipline in constant evolution, where advancements in medical knowledge, technology, education and societal trends converge. It adapts to a highly mutable landscape and is permeable to emerging trends and innovations. Among the most transformative developments in recent years is the widespread proliferation of artificial intelligence (AI), that is revolutionizing all sectors of society, including healthcare and education. In this thesis, AI is succinctly defined, on its evolution and key characteristics. Central to this dissertation is the exploration of the intersection between AI and medical education. A review of literature reveals the myriad applications of AI-associated technologies across different domains of medical education. From automated assessment and feedback mechanisms to personalized learning platforms, virtual patient simulations, and intelligent tutoring systems, AI-driven tools offer unparalleled opportunities for enhancing learning outcomes and optimizing educational processes. Evidence suggests that the integration of AI-based strategies produces tangible benefits, facilitating the generation of teaching materials, delivering effective feedback, and enhancing performance at both educational and operational levels. However, despite the clear advantages demonstrated by these technologies, their widespread adoption in medical school remains limited. More, AI is not a topic in most programs. This gap leaves students and healthcare professionals with a unilateral understanding of AI, overlooking its potential to revolutionize healthcare delivery and medical education. Addressing this disparity is paramount, as it ensures that future generations of healthcare providers are equipped with the requisite skills and knowledge to navigate a rapidly evolving healthcare landscape.
Autores principais:Almeida, Hugo Miguel do Vale Leite Santos de
Assunto:Artificial Intelligence Assessment and Feedback Curriculum Design Medical education Personalized Learning Aprendizagem Personalizada Avaliação e Orientações Desenho Curricular Educação médica Inteligência Artificial
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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