Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education.

Using Automated Scoring to Evaluate Written Responses in English and French on a High-Stakes Clinical Competency Examination.

We present a framework for technology-enhanced scoring of bilingual clinical decision-making (CDM) questions using an open-source scoring technology and evaluate the strength of the proposed framework using operational data from the Medical Council of Canada Qualifying Examination. Candidates' responses from six write-in CDM questions were used to develop a three-stage-automated scoring framework. In Stage 1, the linguistic features from CDM responses were extracted.

Automated essay scoring and the future of educational assessment in medical education.

Context: Constructed-response tasks, which range from short-answer tests to essay questions, are included in assessments of medical knowledge because they allow educators to measure students' ability to think, reason, solve complex problems, communicate and collaborate through their use of writing. However, constructed-response tasks are also costly to administer and challenging to score because they rely on human raters. One alternative to the manual scoring process is to integrate computer technology with writing assessment.