Gen-Mentor: A human-in-the-loop instructional framework for dental radiography using generative AI

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Integrating artificial intelligence into medical imaging offers potential for dental education. While existing models automate diagnosis, they often lack the interpretive depth needed for comprehensive student training. To address these limitations, this paper presents Gen-Mentor , a human-in-the-loop instructional framework that integrates the DentDiff-VLM backbone into a dental-radiography workflow. The backbone uses Faster R-CNN to localize four target radiographic findings: Filling, Implant, Impacted Tooth, and Cavity. A conditional diffusion model supports curriculum expansion by generating class-specific synthetic ROI candidates as candidate instructional assets. A vision–language model (VLM) generates evidence-linked caption candidates, which a large language model (LLM) reformats into candidate case descriptions, comparisons, and quiz prompts. Selected candidate instructional assets then undergo structured expert review. We evaluate Gen-Mentor across technical performance, expert review of instructional assets, and learner acceptance among dental students ( N = 45 ). The framework achieved a mean System Usability Scale score of 72.7, with improvements in case diversity and immediate-feedback support.