Evaluating artificial intelligence large language models in dental education: a cross-sectional survey on usage, perceptions, and integration at a U.S. dental school - Report - DentalSpire
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Evaluating artificial intelligence large language models in dental education: a cross-sectional survey on usage, perceptions, and integration at a U.S. dental school
Clinical Report: Assessing the Role of Large Language Models in Dental Education
Overview
This study evaluates the usage and perceptions of Large Language Model (LLM) tools among faculty and students at the UTHealth School of Dentistry. Findings indicate a higher usage among students, who perceive these tools as beneficial, while faculty express a strong demand for AI training.
Background
The integration of artificial intelligence (AI) in dental education is an emerging area of interest, with potential to enhance educational efficiency and learning outcomes. Despite the promise of LLMs like ChatGPT and Grammarly AI, their adoption in dental education has not been thoroughly explored. Understanding the current usage patterns and attitudes towards these tools is crucial for developing effective training and integration strategies.
Data Highlights
Group
Usage Rate
Perceived Benefit
Demand for Training
Faculty
66%
Lower
Higher
Students
73%
Higher
Lower
Key Findings
66% of faculty and 73% of students reported using LLM-based AI tools.
Students perceived LLM-based AI tools as more beneficial compared to faculty (p < 0.01).
Faculty showed a stronger demand for AI training compared to students (p < 0.05).
Gender differences were noted, with males more supportive of AI in research tasks (p < 0.05).
Students rated ChatGPT more favorably across all categories compared to faculty.
Clinical Implications
The findings highlight the need for structured AI training programs for faculty to enhance their integration of LLM tools in education. Additionally, the positive perception of these tools among students suggests a growing acceptance that could influence future educational practices.
Conclusion
The study underscores the increasing relevance of LLM-based AI tools in dental education and the necessity for tailored training to optimize their use among faculty and students.
