Trial reportBMC oral health2026
Effectiveness of ChatGPT-assisted and drawing-based learning in enhancing understanding of intraoral radiographic anatomy among dental students: a comparative study.
Trial report in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate 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. MATERIALS AND
methodsThis 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).
resultsChatGPT 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).
conclusionChatGPT 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.