AIMC Topic: Curriculum

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GSSCL: A framework for Graph Self-Supervised Curriculum Learning based on clustering label smoothing.

Neural networks : the official journal of the International Neural Network Society
Graph self-supervised learning is an effective technique for learning common knowledge from unlabeled graph data through pretext tasks. To capture the interrelationships between nodes and their essential roles globally, existing methods use clusterin...

Exploring the integration of artificial intelligence in radiology education: A scoping review.

Current problems in diagnostic radiology
BACKGROUND: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current lands...

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties.

BMC medical education
BACKGROUND: The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary st...

Surface and deep learning: a blended learning approach in preclinical years of medical school.

BMC medical education
BACKGROUND: Significant challenges are arising around how to best enable peer communities, broaden educational reach, and innovate in pedagogy. While digital education can address these challenges, digital elements alone do not guarantee effective le...

The use of artificial intelligence for graduate nursing education: An educational evaluation.

Journal of the American Association of Nurse Practitioners
With artificial intelligence (AI) rapidly advancing, advanced practice nurses must understand and use it responsibly. Here, we describe an assignment in which Doctor of Nursing Practice (DNP) students learned to use generative text AI. Using our prog...

Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications.

JMIR medical education
BACKGROUND: Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical educati...

Utilizing natural language processing to analyze student narrative reflections for medical curriculum improvement.

Medical teacher
MOTIVATION: Medical curricula improvement is an ongoing process to keep material relevant and improve the student's learning experience to better prepare them for patient care. Many programs utilize end-of-year evaluations, but these frequently have ...

Reforming China's Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era.

JMIR medical education
China's secondary vocational medical education is essential for training primary health care personnel and enhancing public health responses. This education system currently faces challenges, primarily due to its emphasis on knowledge acquisition tha...