Integrating Artificial Intelligence in Dental Education: Opportunities, Challenges, and Strategies for Future-Ready Curricula

Shaping the Future of Dental Education: A Scoping Review of Artificial Intelligence Integration Strategies

Artificial intelligence (AI) is making significant inroads into dental education, offering new methods such as adaptive learning, virtual simulations, and improved diagnostic tools. While AI presents many opportunities to enhance curricula and clinical training, it also brings challenges related to ethics, legal frameworks, and varying levels of readiness among educators and students.

This article reviews 15 studies published between 2020 and 2024 that explore AI’s role in dental education. It summarizes key findings on curriculum integration, clinical applications, preparedness of faculty and students, and ethical considerations. The goal is to provide practical insights into how AI can be effectively incorporated to improve learning outcomes and clinical skills preparation.

Introduction & Background

Advances in AI technology are poised to change how dental education is delivered. From adaptive learning platforms that tailor content to individual needs to virtual simulations that allow safe hands-on practice, AI tools are bridging the gap between theory and clinical work.

Technologies such as virtual reality (VR), augmented reality (AR), and AI-powered assessment tools are becoming more common in dental training programs. These tools not only improve knowledge acquisition but also expose students to the kinds of AI applications they will encounter in professional practice.

Despite these benefits, integrating AI into dental education is not without obstacles. Privacy concerns, algorithmic bias, and a lack of unified guidelines pose significant challenges. Moreover, the success of AI adoption depends heavily on fostering a culture of acceptance and readiness among faculty and students.

This review critically examines existing AI initiatives in dental education, aiming to highlight what works, what gaps remain, and how institutions can better prepare their communities for AI-enhanced learning.

Review

Aims of the Study

The review investigates current strategies for integrating AI in dental education, assessing their effectiveness and limitations. It also identifies research gaps, ethical and regulatory challenges, and suggests frameworks to improve teaching methods and learning outcomes with AI.

Methods

• Research Question: How is AI being integrated into dental education compared to traditional methods in terms of effectiveness, preparedness, and ethical implications?
• Study Selection: Included studies published in English between 2020 and 2024, focusing on AI use in dental education, diagnostics, or ethical concerns.
• Data Sources: Searched PubMed, Scopus, Web of Science, and Cochrane Library using relevant keywords. Additional articles were identified through reference lists.
• Inclusion Criteria: Quantitative and qualitative research, systematic and literature reviews, and observational studies relevant to AI integration in dental education.
• Exclusion Criteria: Non-dental education AI studies, commentaries, editorials, conference abstracts, or studies lacking sufficient data and methodological clarity.
• Data Extraction: Focused on study design, geographic context, AI technologies used, participant details, outcomes, and study limitations.

Results

The screening process yielded 15 studies for detailed analysis, including systematic reviews, literature reviews, observational studies, and surveys. These studies spanned regions such as Asia, the Middle East, Europe, and the United States.

AI technologies discussed included machine learning, deep learning, virtual simulations, chatbots, and adaptive learning platforms. Participants were primarily dental students and educators.

The findings clustered around four main themes:

• Curriculum Integration: AI tools like AR/VR and diagnostic models are increasingly embedded in dental curricula but require clear ethical standards and integration strategies.
• Diagnostic and Clinical Applications: AI enhances early disease detection, radiographic analysis, and personalized treatment planning.
• Educator and Student Preparedness: Awareness and training gaps exist, though attitudes toward AI are generally positive.
• Ethical and Regulatory Issues: Data privacy, algorithmic bias, and lack of comprehensive regulations remain key concerns.

Discussion

Curriculum Integration and Future Preparedness

Integrating AI into dental curricula is essential. Tools such as generative AI, AR/VR, and deep learning algorithms offer new ways to teach and train dental students. However, these integrations must come with clear ethical guidelines and practical methods to ensure they serve educational goals effectively.

Diagnostic and Clinical Applications

AI supports more accurate diagnostics and treatment planning, helping clinicians deliver better patient care. Machine learning and deep learning tools enhance imaging analysis and facilitate personalized approaches to dental health.

Educator and Student Readiness

Faculty and student preparedness significantly influence AI adoption in dental education. While enthusiasm for AI is generally high, gaps in formal training and resources limit effective implementation.

Ethical and Regulatory Challenges

Concerns around data privacy, algorithmic bias, and regulatory oversight continue to slow AI’s full integration into dental education and practice. Addressing these challenges is critical for building trust and ensuring responsible AI use.

Limitations of the Research

The reviewed studies vary widely in design and scope, which affects comparability and generalizability. Many rely on theoretical frameworks or small, localized samples. More large-scale, multicenter, and longitudinal studies are needed to strengthen the evidence base.

Conclusions

AI holds considerable promise for improving dental education and clinical practice. Emerging technologies can enhance diagnostics, personalize learning, and modernize teaching methods. Realizing this potential depends on updating curricula, investing in faculty and student training, and establishing ethical and regulatory frameworks.

Current research is limited by small sample sizes and regional focus. To move forward, the dental education community should prioritize comprehensive studies and develop standardized guidelines for AI integration. Overcoming ethical and practical challenges will be key to embedding AI successfully and sustainably.

For educators interested in expanding their AI knowledge and skills, exploring AI courses designed for professionals can be a practical next step.