Researchers at the National University of Singapore Faculty of Dentistry have developed a scalable, transcript-based assessment method that significantly streamlines pediatric dental training. In a recent study published in JMIR Medical Education, investigators Dr. Ishreen Kaur, Dr. Gabriel Lee, and Associate Professor Hu Shijia demonstrated that evaluating student performance through text transcripts of clinical sessions yields accuracy comparable to traditional full-video reviews. Managing pediatric patients remains a high-stress challenge for dental trainees, with prior research indicating stress levels three times higher than those of experienced specialists. Conventional training relies heavily on faculty reviewing lengthy video recordings to deliver personalized behavior-guidance feedback, a process that strains limited academic resources. The new transcript-based approach eliminates video analysis while preserving assessment precision, allowing educators to allocate more time to individualized mentorship. According to Associate Professor Hu, this shift enables more targeted feedback on student-patient interactions, fostering greater clinical confidence and ultimately improving care quality for young patients. Because transcripts are inherently digital, the methodology creates a direct pathway for artificial intelligence integration. The research team envisions developing an AI-powered virtual mentor capable of analyzing student transcripts in real time, generating immediate behavioral insights, and reducing administrative burdens on teaching staff. If validated, this framework could extend beyond dentistry to broader healthcare disciplines, including nursing and general medicine, to standardize and scale pediatric communication training. The NUS team is now preparing a follow-up study to evaluate the clinical efficacy of large language models in delivering AI-generated feedback during live pediatric dental sessions, marking a significant step toward automated, data-driven clinical education.