AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review - Scorecard - DentalSpire
Clinical Scorecard: Language Technologies Powered by AI for Enhancing Learning and Clinical Communication in Global Undergraduate Dental Education: A Scoping Review
At a Glance
Category
Detail
Condition
AI-enabled language technologies in dental education
Key Mechanisms
Generative AI, large language models, neural machine translation
Target Population
Undergraduate dental students, particularly those with limited English proficiency
Care Setting
International dental education
Key Highlights
AI technologies can assist in terminology translation and communication rehearsal.
Evidence base includes 36 non-policy sources and four policy documents.
Challenges include cognitive demands and communication breakdowns in clinical settings.
Implementation requires validated resources, educator supervision, and privacy protections.
Limited evidence on direct clinical outcomes and learner perceptions.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
Inaccurate translation and fabricated information due to limited language resources.
Patient & Prescribing Data
Not directly assessed; focuses on dental undergraduates and their communication needs.
AI technologies may enhance language acquisition and communication training.
Clinical Best Practices
Utilize AI technologies for multilingual tutoring and reflective writing support.
Ensure staged rehearsal of communication skills before patient exposure.
Maintain academic integrity and privacy protections in AI implementation.
