An AI-Driven H5P Training for Epilepsy Emergency Management Preparedness in Dental Education

Deficiencies in knowledge and skills related to the management of medical emergencies in dental settings can adversely affect the clinical safety of patient safety. This intervention study evaluates the effect of the 'Medical Emergencies in Dentistry: Epilepsy Management' module delivered via the H5P platform on postgraduate dental residents' knowledge of epilepsy management and their artificial intelligence (AI) readiness.

The study was based on a single group pre-test-post-test design, conducted with thirteen postgraduate dental residents. A two-day, fully online epilepsy-management module was designed and implemented, integrating H5P platform activities within Moodle, as well as an AI-assisted scenario-writing task using Gemini. The potential impact of the intervention was evaluated using a range of data collection tools, including a knowledge test, the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), quizzes, the Facilitator Guidance Evaluation Form, the End-of-Module Evaluation Form and a scenario evaluation form. Language editing assistance was obtained from ChatGPT during the translation and preparation of the manuscript; all content was reviewed and approved by the authors.

Post-test scores assessing knowledge increased significantly compared with pre-test scores (p = 0.017), and MAIRS-MS scores also showed significant improvement after training (p = 0.001). The end-of-module evaluation revealed high mean scores across domains, particularly for the H5P platform, clinical practice perception and AI application. Overall, participants expressed positive views regarding usability, learning impact and clinical applicability, with only minor concerns about technical issues and scheduling.

The epilepsy management training module provided significant improvements in epilepsy management knowledge and AI readiness among postgraduate dental residents, while also receiving high ratings for usability and clinical relevance. However, given the limited sample size, the findings should be interpreted with caution. Accordingly, the findings are expected to provide preliminary insights and guidance for future research on larger samples.