AIMC Topic: Educational Measurement
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ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.
BMC oral health
Feb 1, 2025
OBJECTIVES: This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to text-based multiple-choice questions related to oral radiology, as featured in the Dental Specialty...
Twelve tips to afford students agency in programmatic assessment.
Medical teacher
Jan 31, 2025
Programmatic assessment is gaining traction in health professions education, emphasizing continuous, integrated appraisal to support holistic student development. Despite its adoption, effectively affording student agency within programmatic assessme...
Empowering medical students with AI writing co-pilots: design and validation of AI self-assessment toolkit.
BMC medical education
Jan 31, 2025
BACKGROUND AND OBJECTIVES: Assessing and improving academic writing skills is a crucial component of higher education. To support students in this endeavor, a comprehensive self-assessment toolkit was developed to provide personalized feedback and gu...
Accuracy of latest large language models in answering multiple choice questions in dentistry: A comparative study.
PloS one
Jan 29, 2025
OBJECTIVES: This study aims to evaluate the performance of the latest large language models (LLMs) in answering dental multiple choice questions (MCQs), including both text-based and image-based questions.
Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework.
PloS one
Jan 22, 2025
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analy...
Claude, ChatGPT, Copilot, and Gemini performance versus students in different topics of neuroscience.
Advances in physiology education
Jan 17, 2025
Despite extensive studies on large language models and their capability to respond to questions from various licensed exams, there has been limited focus on employing chatbots for specific subjects within the medical curriculum, specifically medical ...
The ChatGPT Fact-Check: exploiting the limitations of generative AI to develop evidence-based reasoning skills in college science courses.
Advances in physiology education
Jan 17, 2025
Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted, as artificial intelligence (AI) can make factual mistakes. This poses challenge...
Performance Evaluation and Implications of Large Language Models in Radiology Board Exams: Prospective Comparative Analysis.
JMIR medical education
Jan 16, 2025
BACKGROUND: Artificial intelligence advancements have enabled large language models to significantly impact radiology education and diagnostic accuracy.
Should oral examination be reimagined in the era of AI?
Advances in physiology education
Jan 15, 2025
As artificial intelligence (AI) continues to reshape education, concerns about the authenticity of student work have escalated, particularly in relation to written assignments influenced by AI-powered tools. This article explores the role of the oral...
Effect of feedback-integrated reflection, on deep learning of undergraduate medical students in a clinical setting.
BMC medical education
Jan 14, 2025
BACKGROUND: Reflection fosters self-regulated learning by enabling learners to critically evaluate their performance, identify gaps, and make plans to improve. Feedback, in turn, provides external insights that complement reflection, helping learners...
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