Effectiveness of ChatGPT-assisted and drawing-based learning in enhancing understanding of intraoral radiographic anatomy among dental students: a comparative study
Accurate identification of intraoral radiographic landmarks is essential for diagnosis; however, complex jaw anatomy presents learning challenges for undergraduate dental students. The educational potential of AI-assisted tools and drawing-based learning for intraoral radiographic anatomy remains unexplored. This study evaluated the effectiveness of ChatGPT-5.0-assisted learning and drawing-based learning as supplementary tools to enhance students’ understanding of intraoral radiographic anatomy. This comparative study included third-year Bachelor of Dental Surgery students. After a standardized lecture, students were randomly assigned to three groups: ChatGPT, self-directed learning, and drawing (n = 26 each), and baseline knowledge was assessed. The ChatGPT group used structured prompts, the drawing-based learning group drew and labelled landmarks, and the self-directed learning group studied independently. A post-intervention assessment was conducted after 12 weeks. Intragroup changes were analysed using paired t-tests with Cohen’s d, and intergroup differences using one-way ANOVA with Eta-squared (η²). ChatGPT and drawing groups showed significantly greater improvement than the Self-directed learning group. The drawing group demonstrated the most consistent gains, with significant improvement in 12 landmarks and moderate-to-large effect sizes (Cohen’s d ≈ 0.45–0.77). The ChatGPT group showed significant improvements in several landmarks, particularly in the maxillary and mandibular anterior regions. Intergroup analysis showed higher post-instructional scores for the drawing group in 20 of 30 landmarks, with moderate-to-large η² values (≈ 0.08–0.25). ChatGPT and drawing-based learning are effective supplementary strategies for learning intraoral radiographic landmarks, outperforming self-directed learning alone. Their integration into dental radiology education may enhance anatomical understanding and long-term learning outcomes.
Authors
- Suresh Kandagal Veerabhadrappa (ORCID: https://orcid.org/0000-0003-3784-594X)
- Vipin Jain (ORCID: https://orcid.org/0000-0001-8168-4077)
- Siddharthan Selvaraj
- Jayanth Kumar Vadivel
- Seema Yadav
- Anand Marya
Institutions
- University of Puthisastra (KH)
- Dr. D.Y. Patil Vidyapeeth, Pune (IN)
- Ingham Institute (AU)
- Western Sydney University (AU)
- Saveetha University (IN)
- SEGi University (MY)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2026-04-14
- DOI
- https://doi.org/10.1186/s12903-026-08307-5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00