Testing an Educational Resource for Implementation in Temporomandibular Disorders Curriculum Through a Phase I Define and Refine Study.

OBJECTIVES

This pilot study assessed the feasibility of a virtual patient-based learning (VPBL) platform as an educational tool to address insufficient clinical exposure to temporomandibular disorder (TMD) cases in predoctoral dental education.

METHODS

The VPBL platform, developed with Articulate Storyline, featured 15 virtual patients (VPs) based on Diagnostic Criteria for TMD. In a two-step study, 40 third- and fourth-year dental students rated the platform's feasibility, acceptability, appropriateness, burden, and usability using standardized Likert-based scales. In Cohort 1, 20 students (65.0% females; 26.9 ± 3.3 years old) evaluated the 15 VPs and provided feedback. Cohort 2-study consisted of adjusting the VPs based on feedback from Cohort 1 and re-piloting the cases on 20 randomly selected 4th-year students (68.4% females; 26.8 ± 1.9 years old). Scores from Cohort 1 and Cohort 2 were compared using independent t-tests; qualitative exit interviews were conducted on all participants.

RESULTS

The Cohort 1 study revealed high ratings in acceptability (4.4 ± 0.5, range 1-5), appropriateness (4.4 ± 0.6, range 1-5), feasibility (4.2 ± 0.5, range 1-5), and usability (73.8 ± 15.2, range 0-100), with a minimal burden (the greatest being the difficulty of use, 1.9 ± 0.6, range 0-4). In the Cohort 2 study, the VPBL was adjusted according to feedback, and five additional VPs were created. Compared to the Cohort 1-study, the modified-VPBL obtained significantly higher scores in acceptability (4.8 ± 0.3, p = 0.015), appropriateness (4.7 ± 0.4, p = 0.035), feasibility (4.7 ± 0.4, p = 0.004), usability (83.6 ± 12.9, p = 0.034), and lower scores in difficulty of use (1.5 ± 0.3, p = 0.022).

CONCLUSIONS

The VPBL platform demonstrated feasibility as an educational tool for integration into the TMD curriculum for third- and fourth-year dental students. These Phase I data provide scientific validation for more rigorous testing on a broader scale.