AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review

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Background International undergraduate dental students often learn and communicate in a language other than their first language, affecting terminology acquisition, clinical reasoning, patient explanations, informed consent, clinical communication, and patient safety. AI-enabled language technologies—including generative artificial intelligence (GenAI), large language models (LLMs), and neural machine translation (NMT)—may support these tasks, but their use in this population has not been systematically mapped. Methods Following the Arksey and O'Malley framework, JBI guidance, and PRISMA-ScR, PubMed, Scopus, and Web of Science were searched from inception. Searches were conducted on 1 March 2026 and updated on 28 March 2026, supplemented by reference-list screening and targeted policy and grey-literature searches. Sources were charted by context, population, technology, outcomes, limitations, risks, and implementation, and classified by relevance. Results Thirty-six non-policy sources and four policy or governance documents were included. Five involved undergraduate dental students, three addressed international, multilingual, or limited-English-proficiency learners or relevant policies, eight concerned other health-professions contexts, and eight were translation or technical benchmarks without learners. Categories overlapped, and only one source combined undergraduate dental students, an international or multilingual population, and an AI-enabled language technology. Applications included terminology translation, multilingual tutoring, communication rehearsal, and reflective-writing support. No source assessed retained learning, transfer to authentic clinical encounters, or patient-level outcomes. Conclusions AI-enabled language technologies may support supervised language learning and patient-safety-oriented communication training, but direct evidence is sparse, short term, and largely derived from adjacent populations or technical benchmarks. Performance varies by language, task, prompt, model version, and context. Pending direct, comparative, longitudinal, and clinically situated evidence, implementation should include validated local resources, educator oversight, academic-integrity boundaries, privacy safeguards, and staged rehearsal before patient exposure.

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