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