Home›Rheumatology› AI-enabled language technologies support dental student communication training despite sparse evidence of clinical outcomes
AI-enabled language technologies support dental student communication training despite sparse evidence of clinical outcomesAI Tools May Support Language Learning for Dental Students
Frontiers in MedicinePublished September 3, 2026DOI ↗Editorial oversight: Dr. Amelia Tan, PhD · Internal Medicine & Chronic Disease
AI-generated summary of the cited source, checked by automated accuracy review.
How we work
Share
Key Takeaway
Note that AI-enabled language tools may support dental communication training but lack evidence for clinical outcomes.
This scoping review synthesizes the current evidence regarding the use of AI-enabled language technologies, such as generative artificial intelligence (GenAI), large language models (LLMs), and neural machine translation (NMT), for undergraduate dental students. The review identifies potential applications in terminology translation, multilingual tutoring, communication rehearsal, and reflective-writing support.
Key findings indicate that direct evidence is sparse. Only one source combined an international or multilingual student population with AI-enabled language technologies. Furthermore, no source assessed retained learning, transfer to clinical encounters, or patient-level outcomes. The existing evidence is largely derived from technical benchmarks or adjacent populations.
Clinical application of these tools may support supervised language learning and patient-safety-oriented communication training. However, the authors note that implementation must include educator oversight, academic-integrity boundaries, and privacy safeguards. Because evidence is short-term and lacks clinical outcome data, a staged rehearsal approach is recommended before students engage in patient exposure.
This review looked at how AI-enabled technologies, such as large language models and machine translation, can help dental students learn new languages. The study specifically looked at tools for translating medical terms, providing multilingual tutoring, and practicing communication with patients.
While the review found that these tools can help with communication rehearsal and writing, the evidence is currently limited. Most of the data comes from short-term studies or from technical tests rather than long-term clinical use. Only one source specifically combined international dental students with AI language tools.
Because the evidence is sparse, these tools should be used with caution. There were no studies that measured if students actually remembered the information long-term or if it improved real-life patient interactions. If used in a classroom, experts suggest using these tools under teacher supervision to ensure accuracy and protect patient privacy before students work with real patients.
What this means for you:
AI tools may help dental students practice language skills, but evidence on long-term learning is currently limited.
Common questions
Can AI help dental students learn to speak other languages?
AI-enabled technologies, including large language models and neural machine translation, can support supervised language learning. These tools can help students with terminology translation, multilingual tutoring, and practicing communication rehearsal before they interact with patients.
Is there evidence that AI helps with long-term learning in dentistry?
The evidence is currently sparse and mostly short-term. No studies in this review assessed whether students retained the information over time or if the training successfully transferred to authentic clinical encounters with patients.
Is it safe for dental students to use AI for patient communication?
AI can help with communication rehearsal, but it should be used with educator oversight and privacy safeguards. Experts suggest using these tools for staged rehearsal before students are exposed to real patients to ensure safety and accuracy.
Study Details
Study typeSystematic review
EvidenceLevel 1
PublishedSep 2026
View Original Abstract ↓
BackgroundInternational 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.MethodsFollowing 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.ResultsThirty-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.ConclusionsAI-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.
