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.

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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
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article

Effectiveness of ChatGPT-assisted and drawing-based learning in enhancing understanding of intraoral radiographic anatomy among dental students: a comparative study

Suresh Kandagal Veerabhadrappa, Vipin Jain, Siddharthan Selvaraj, Jayanth Kumar Vadivel et al.
BMC Oral Health
Artificial Intelligence in Healthcare and Education
article

Effectiveness of ChatGPT-assisted and drawing-based learning in enhancing understanding of intraoral radiographic anatomy among dental students: a comparative study

Suresh Kandagal Veerabhadrappa, Vipin Jain, Siddharthan Selvaraj, Jayanth Kumar Vadivel, Seema Yadav, Anand Marya
article en

Abstract

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.

BMC Oral Health
University of Puthisastra (KH), Dr. D.Y. Patil Vidyapeeth, Pune (IN), Ingham Institute (AU), Western Sydney University (AU), Saveetha University (IN), SEGi University (MY)
Quality Education
Openalex Percentile: Top 25%
Artificial Intelligence in Healthcare and Education
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