Actionable companion · Assessment and evidence of learning

From evidence to district action: Panoramic Landmarks: Comparing LLM-Assisted, Manual Tracing, and Self-Directed Learning in Dental Education

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Accurate identification of anatomical landmarks on panoramic radiographs is a foundational yet challenging skill in dentistry. Traditional didactic teaching often requires supplementation to achieve proficiency. This study evaluates and compares the efficacy of three supplementary learning modalities: self-directed learning (SDL), traditional manual tracing (MT), and an AI-driven approach using ChatGPT. Methods In this prospective study, 63 third-year dental students were assigned to one of three groups (n = 21 each): SDL, MT, or ChatGPT-assisted learning. Following a theoretical lecture, students were assessed using a 30-item test immediately after the lecture (baseline) and again at a 4-week follow-up. Intra- and intergroup differences were analysed using Wilcoxon signed-rank and Kruskal–Wallis tests, respectively. Results Intergroup analysis demonstrated that the MT group achieved significantly higher overall scores than both the SDL and ChatGPT groups (P < .05), correctly identifying the most landmarks (26/30). Within-group analysis revealed significant improvements from baseline in the MT group for 24 landmarks (P < .05 for key structures like the hard palate and hyoid bone) and in the ChatGPT group for 16 landmarks (P < .05 for the glossopharyngeal air space). The SDL group showed no significant improvement. Notably, the ChatGPT group outperformed MT in identifying four specific landmarks, including the zygomatic process and nasopharyngeal air space. Conclusion For optimal learning in dental radiology, an integrated approach is recommended. MT proved most effective overall, while ChatGPT added value for specific landmarks. Combining both methods may further enhance student proficiency. Clinical Relevance Identification of landmarks is essential for accurate diagnosis and treatment planning. This study demonstrates that MT significantly enhances landmark recognition, while ChatGPT provides supplementary value. Integrating traditional and AI-assisted methods may further strengthen dental radiology education. Key words: Artificial intelligence, Dental, Education sustainability, Landmarks, Panoramic radiography, Tracing

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Suresh Kandagal Veerabhadrappa; Jayanth Kumar Vadivel; Seema Yadav Roodmal; Thantrira Porntaveetus; Anand Marya; Siddharthan Selvaraj. Panoramic Landmarks: Comparing LLM-Assisted, Manual Tracing, and Self-Directed Learning in Dental Education. International Dental Journal (2026-01-22). https://doi.org/10.1016/j.identj.2025.109393

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.