AIMC Topic: Education, Medical, Undergraduate

Clear Filters Showing 11 to 20 of 105 articles

Artificial intelligence-based chatbots improve the efficiency of course orientation among medical students: a cross-sectional study.

BMC medical education
BACKGROUND: Large language models (LLMs) like ChatGPT offer new ways to improve academic and administrative workflows in medical education, particularly for students studying in a language that is not their native tongue. We set out to examine whethe...

Large language models as educational collaborators: developing non-conventional teaching aids in pharmacology & therapeutics.

BMC medical education
BACKGROUND: With the growing integration of artificial intelligence in medical education, this study compares the quality and educational robustness of content generated by two large language models (LLMs), DeepSeek-V3 and ChatGPT 4.0, on the emergin...

Understanding how medical students learn in the era of artificial intelligence: a mixed methods study.

BMC medical education
BACKGROUND: As medical education evolves, current teaching practices often remain misaligned with how today's digitally native students prefer to learn. While the use of digital tools is widespread, there is limited clarity on students' learning beha...

Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs.

BMC medical education
OBJECTIVE: To evaluate the feasibility and educational value of employing large language models (LLMs) to generate clinical case scenario with multiple-choice questions (MCQs) for undergraduate medical education.

Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context.

JMIR medical education
BACKGROUND: Effective communication is fundamental to high-quality health care delivery, influencing patient satisfaction, adherence to treatment plans, and clinical outcomes. However, communication skills training for medical undergraduates often fa...

AI's Accuracy in Extracting Learning Experiences From Clinical Practice Logs: Observational Study.

JMIR medical education
BACKGROUND: Improving the quality of education in clinical settings requires an understanding of learners' experiences and learning processes. However, this is a significant burden on learners and educators. If learners' learning records could be aut...

Perception of Medical Undergraduates on Artificial Intelligence in Medical Education: Qualitative Exploration.

JMIR medical education
BACKGROUND: Artificial intelligence (AI) has revolutionized medical education by delivering tools that enhance and optimize learning. However, there is limited research on the medical students' perceptions regarding the effectiveness of AI as a learn...

Development of a Clinical Clerkship Mentor Using Generative AI and Evaluation of Its Effectiveness in a Medical Student Trial Compared to Student Mentors: 2-Part Comparative Study.

JMIR medical education
BACKGROUND: At the beginning of their clinical clerkships (CCs), medical students face multiple challenges related to acquiring clinical and communication skills, building professional relationships, and managing psychological stress. While mentoring...

The role of generative AI tools in case-based learning and teaching evaluation of medical biochemistry.

BMC medical education
BACKGROUND: Medical biochemistry, a fundamental course in medical education, has a complex and expanding knowledge base. Traditional teaching methods often fail to meet students' needs for in-depth understanding and personalized learning. Students ca...

Artificial intelligence assisted automated short answer question scoring tool shows high correlation with human examiner markings.

BMC medical education
BACKGROUND: Optimizing the skill of answering Short answer questions (SAQ) in medical undergraduates with personalized feedback is challenging. With the increasing number of students and staff shortages this task is becoming practically difficult. He...