โก Quick Summary
This study investigated the use of a custom-trained Large Language Model (LLM) to support journal club (JC) preparation and discussion among dental residents. The findings indicated that the LLM significantly improved residents’ comprehension, confidence, and engagement during discussions.
๐ Key Details
- ๐ Study Duration: March-September 2024
- ๐ฅ Participants: 16 postgraduate dental residents across two subspecialties
- ๐ ๏ธ Methodology: Design-based research approach with structured observations and feedback
- ๐ Sessions Conducted: Six journal club sessions
๐ Key Takeaways
- ๐ LLM Usage: Residents utilized the LLM for summarizing articles and clarifying complex statistical methods.
- ๐ Positive Feedback: 53% of residents reported a positive experience using the LLM for JC preparation.
- ๐ค Neutral Responses: 43% of residents were neutral about their experience.
- โ Negative Feedback: Only one resident reported a negative experience.
- ๐ Challenges: Issues included the need for precise prompt construction and occasional inaccuracies in content.
- ๐ก Key Competencies: Prompt-writing skills, critical thinking, and AI literacy were identified as essential for effective LLM use.
- ๐ฉโ๐ซ Faculty Observations: Faculty noted enhanced participation but emphasized the importance of critically evaluating LLM outputs.
๐ Background
Journal clubs are vital in medical education, fostering critical appraisal and evidence-based practice. However, residents often encounter barriers such as limited time and challenges in grasping complex concepts and statistics. The integration of technology, particularly Large Language Models, presents an innovative solution to enhance participation and learning in these settings.
๐๏ธ Study
Conducted over several months, this study aimed to explore the effectiveness of a custom-trained LLM in aiding journal club preparation among dental residents. The researchers employed a design-based research approach, integrating the LLM with relevant literature to facilitate discussions and improve understanding of complex topics.
๐ Results
The study revealed that the LLM significantly improved residents’ comprehension of complex content and boosted their confidence during discussions. The majority of residents found the tool beneficial for summarizing articles and generating discussion points, indicating a positive shift in engagement levels. However, challenges such as the need for precise prompt construction and occasional inaccuracies were noted.
๐ Impact and Implications
The findings suggest that LLMs can serve as valuable adjuncts to traditional teaching methods, enhancing engagement and comprehension in journal clubs. As generative AI continues to evolve, further research is warranted to explore its broader implications on learners’ cognitive processes, epistemic trust, and educational equity. This could lead to more effective educational strategies in medical training.
๐ฎ Conclusion
This study highlights the potential of LLMs to transform journal club participation among residents. By supporting deeper engagement and comprehension, these technologies can complement traditional educational methods. As we look to the future, ongoing research into the integration of AI in medical education will be crucial for optimizing learning experiences and outcomes.
๐ฌ Your comments
What are your thoughts on the use of AI in medical education? Do you believe LLMs can enhance learning experiences? Let’s discuss! ๐ฌ Leave your comments below or connect with us on social media:
Exploring an LLM’s Use in Supporting Journal Club Preparation and Discussion Among Residents.
Abstract
INTRODUCTION: Journal clubs (JCs) play an important role in medical education by promoting critical appraisal and evidence-based practice. However, residents often face barriers to effective participation. Some of the issues that are commonly faced include limited time and difficulty understanding complex concepts and statistics.
METHODS: This study was conducted during March-September 2024 and explored the use of a custom-trained Large Language Model (LLM) as a supportive tool for JC preparation and participation among postgraduate dental residents. Using a design-based research approach, researchers implemented the LLM integrated with relevant literature. Six JC sessions were conducted with sixteenย residents across two subspecialties, accompanied by structured observations, feedback forms, and pre-/and post-focus groups with residents and faculty (nย =ย 16).
RESULTS: Findings revealed that the LLM improved residents’ comprehension of complex content, enhanced confidence, and increased engagement during discussions. Residents used the tool for summarizing articles, clarifying statistical methods, and generating discussion points. Fiftythree percent reported a positive experience of using the LLM for JC preparation, Forty-three percentย were neutral, and only one response was negative. However, challenges included the need for precise prompt construction, occasional content inaccuracies, and limited depth in some specialized areas. Faculty observed enhanced participation but stressed the need for critical evaluation of LLM outputs. Both groups identified prompt-writing skills, critical thinking, and AI literacy as key competencies for effective LLM use.
CONCLUSIONS: LLMs can complement traditional teaching by supporting deeper engagement in JCs. As generative AI evolves, further research should examine its broader implications on learners’ cognitive processes, epistemic trust, and educational equity.
Author: [‘Umer F’, ‘Mansoor A’, ‘Naseem A’, ‘Kazmi SMR’]
Journal: J Dent Educ
Citation: Umer F, et al. Exploring an LLM’s Use in Supporting Journal Club Preparation and Discussion Among Residents. Exploring an LLM’s Use in Supporting Journal Club Preparation and Discussion Among Residents. 2025; (unknown volume):(unknown pages). doi: 10.1002/jdd.70072