AI and the double competence gap in dental education: towards symbiotic intelligence

Artificial intelligence (AI) is increasingly becoming part of the knowledge infrastructure of healthcare. From diagnostic support and image interpretation to patient communication and clinical decision-making, AI-supported technologies, including generative AI, are changing the conditions under which healthcare professionals work. Dentistry is no exception. Recent research highlights emerging applications of AI in dental practice, while international reviews suggest growing opportunities and challenges associated with integrating AI into dental education. However, the rapid development of AI-supported technologies raises questions about how effectively professional education and continuing professional development can respond to evolving competency requirements. This raises a central question: How can dental education and continuing professional development respond to the potential double competence gap associated with AI-enabled dental practice?In this Opinion article, we address this question by proposing a conceptual framework that distinguishes between an initial education-practice gap and a continuing professional competence gap. Drawing on the concepts of AI literacy, Professional Digital Competence, and symbiotic intelligence, we discuss how dental education may develop and assess the competencies required for critical, ethical, and responsible AI use while preserving independent clinical reasoning, professional accountability, and patient-centred care.The rapid development of artificial intelligence raises important questions about how dental education can maintain the relevance of its curricula while safeguarding established standards of clinical competence, patient safety, and professional accountability. Although international reviews indicate growing interest in AI applications within dental education, systematic curricular integration appears to vary across institutions and educational contexts (9,10,24). Such variation should not necessarily be interpreted as resistance to technological change, as cautious implementation may also reflect legitimate concerns about educational quality, clinical validity, ethical safeguards, and the availability of robust evidence. Preliminary analyses of regulations and national curriculum frameworks for professional education raise questions about whether current educational provision adequately addresses emerging AIrelated competency requirements, potentially contributing to the two dimensions of the proposed double competence gap. Preliminary analyses of regulations and national curriculum frameworks for professional education suggest that this may contribute to reinforcing an already existing double competence gap When generative artificial intelligence became widely accessible through platform-based services such as ChatGPT in the autumn of 2022, many assumed that it would primarily affect the technology sector. However, when we tested a professional version of ChatGPT (GPT-4) in medicine, and partly in dental and psychology education, in March 2023, it was already apparent that this represented a technological paradigm shift with potentially farreaching implications across professional education programmes (1,2).While AI capabilities have developed rapidly in recent years in the health sciences (3)(4)(5)(6)(7)(8)(9)(10)(11), their integration into dental education presents a different set of challenges. Curriculum development, faculty preparation, assessment design, and clinical governance require careful consideration and cannot necessarily follow the pace of technological innovation. Recent reviews indicate that AI-related educational initiatives are emerging, but their integration into formal curricula and assessment practices remains variable. Several key explanations emerge from the literature: already overcrowded curricula, insufficient profession-specific digital competence among educators, and a lack of structured implementation (9,10,24). This potential misalignment between technological developments and structured educational provision provides an important rationale for examining the proposed double competence gap.Dental education has previously incorporated technologies such as digital radiography, simulation, intraoral scanning, and computer-aided design and manufacturing (29). These developments offer relevant lessons for AI integration: new technologies require faculty development, opportunities for supervised practice, alignment with clinical tasks, and assessment of what students can actually do. However, the current AI transition differs in its breadth and pace. Generative AI can be used across clinical reasoning, communication, learning, and assessment, including through tools that students access independently of their institutions. Moreover, the capabilities and limitations of these systems may change within a single curriculum cycle. Dental programmes should therefore draw on established approaches to curricular implementation while developing a more continuous process for reviewing AIrelated learning outcomes, teaching cases, and assessment practices.The Norwegian national curriculum regulations for healthcare professional education (RETHOS) emphasise that educational programmes should respond to society's current and future competence needs. However, AI is not explicitly addressed in the national curriculum regulations for dentistry, medicine, or psychology (12)(13)(14). Similarly, the Norwegian Agency for Quality Assurance in Education (NOKUT)'s national evaluation of medical education in 2025 provides limited explicit discussion of AI (15). These observations should be interpreted with caution. The absence of explicit AI terminology in national regulations or evaluation reports does not necessarily imply that AIrelated competencies are absent from institutional curricula, teaching practices, or assessment arrangements. National frameworks may deliberately emphasise enduring, technology-neutral professional competencies, allowing educational institutions to adapt their programmes to technological developments without requiring frequent regulatory revisions.Nevertheless, the limited explicit attention to AI in these national documents raises relevant questions about how emerging AI-related competencies are interpreted, operationalised, and assessed within individual educational programmes. International reviews have identified variation in the integration of AI into dental education, including challenges related to curriculum development, faculty preparedness, and the assessment of AI-related competencies (9,10,24).From the perspective of the proposed double competence gap framework, the central concern is therefore not simply whether AI is explicitly mentioned in national policy documents, but whether educational programmes systematically develop and assess the competencies required for responsible AI use in dental practice. Addressing this question requires empirical examination of institutional curricula, learning outcomes, educational activities, and assessment practices rather than relying solely on national policy analysis.Although the policy examples discussed here are drawn from Norway, the distinction between national competency frameworks, institutional curricula, and actual educational practices may also be relevant internationally. However, the extent to which the proposed double competence gap applies across different countries and dental education systems requires context-specific empirical investigation.The increasing integration of AI into healthcare knowledge infrastructures may also have epistemological implications for professional education. As AI systems become involved in information retrieval, diagnostic support, and clinical decision-making, questions arise concerning how professional knowledge is produced, evaluated, interpreted, and applied. These developments may gradually reshape aspects of professional roles and clinical reasoning, although their scope, pace, and educational consequences remain uncertain. Rather than predicting a specific timeline for such changes, dental education should develop adaptable approaches that enable students and practising professionals to critically evaluate emerging technologies while maintaining established standards of clinical expertise and professional accountability (16,18). Professional digital competence and AI literacy should become integral components of both the knowledge infrastructure and professional competence, and conceptual frameworks need to be developed that account for professionspecific AI use across both urban and rural patient contexts (17).At the same time, the challenge extends far beyond today's dentist education. The vast majority of today's practising professionals completed their education long before generative artificial intelligence became part of professional practice. Consequently, they have not necessarily received systematic training in how such AI systems can be used professionally, what their limitations are, or which ethical and legal challenges they may entail.We propose the concept of a double competence gap to describe two distinct but interconnected potential mismatches between the competencies required for responsible professional practice in increasingly AI-enabled dental healthcare and the competencies systematically developed through initial dental education and continuing professional development. A competence gap is understood here as a discrepancy between demonstrable professional capabilities and the knowledge, skills, and critical judgement required to perform relevant professional tasks safely, effectively, and ethically. Importantly, such gaps cannot be inferred solely from the availability of new technologies or the absence of explicit AI terminology in educational policy documents; their existence and extent require empirical investigation.The first competence gap, which we term the initial education-practice gap, concerns the potential misalignment between undergraduate dental education and the evolving competencies required in AI-enabled dental practice. International reviews suggest that the systematic integration of AI-related learning outcomes, teaching activities, and assessment practices in dental education remains uneven (9,10,24). This raises questions about whether existing educational provision adequately prepares students for the critical and clinically appropriate use of AI in future dental practice. Although students may independently engage with generative AI and other AI-supported technologies, such engagement does not necessarily constitute systematic development of profession-specific AI literacy. The central educational concern is therefore whether curricula, learning outcomes, teaching practices, and assessment arrangements adequately prepare students to critically evaluate AI-generated information, recognise its limitations, and integrate appropriate AI-supported decisionmaking with established clinical knowledge and professional judgement.The second competence gap, termed the continuing professional competence gap, concerns practising dentists whose initial professional education preceded the widespread availability of contemporary generative AI and other emerging AI applications. These professionals may encounter new technological expectations without having had equivalent opportunities for structured education in AI evaluation, clinical applicability, patient safety, data protection, and professional accountability. However, the extent of this gap is likely to vary across practitioners, clinical settings, previous digital experience, and access to continuing professional development.The conceptual contribution of the double competence gap framework lies in connecting these two educational challenges across the professional life course. Whereas AI literacy describes a set of relevant competencies, and Professional Digital Competence provides a broader perspective on technology-related professional capabilities, the double competence gap framework draws attention to the potential misalignment between competency requirements and educational provision at two different stages of professional development. It thereby offers a basis for examining how initial education, curriculum and assessment, faculty development, and continuing professional development can respond to changing professional requirements.We present the double competence gap as a conceptual and empirically testable proposition rather than as an established finding across dental education. Its educational significance depends on whether these proposed mismatches can be identified, assessed, and addressed in specific institutional and professional contexts.Empirically investigating the proposed double competence gap requires distinguishing between shortcomings in educational provision and demonstrable deficiencies in professional competence. The initial education -practice gap could be examined through curriculum mapping, IADT-guidelines (34IADT, 2026), analysis of intended learning outcomes and assessment practices, interviews with educators and students, and performance-based assessments of AI-related clinical competencies. The continuing professional competence gap could be investigated through research informed knowledge (33e.g. Ji et al., 2025), surveys, interviews, and scenario-based assessments involving practising dentists with different levels of professional experience and AI exposure. Importantly, the absence of explicit AI-related learning outcomes or structured training would indicate a potential gap in educational provision, but would not, in itself, demonstrate insufficient professional competence. Establishing a competence gap would require comparing observable performance with independently defined, clinically relevant competency standards, while accounting for differences in professional roles, practice contexts, and access to continuing professional development.Addressing the proposed double competence gap requires conceptual clarity regarding AI literacy, Professional Digital Competence (PDC), and symbiotic intelligence (SI). Although these concepts are closely related, they represent distinct dimensions of professional competence and human -AI interaction in dental education and practice.AI literacy refers to the knowledge, skills, and critical judgement required to understand, evaluate, and appropriately use AI systems within specific professional contexts. In dentistry, this includes the capabilities and limitations of AI, critically information clinical and relevant potential and patient information, and maintaining professional accountability. AI literacy therefore extends beyond to clinical reasoning, ethical and informed Digital Competence a of professional competence the critical, ethical, and clinically appropriate use of digital dentistry, includes established digital practices such as digital radiography, patient intraoral scanning, and clinical as as emerging AI applications. 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Rather than AI as an technological we propose relevant AIrelated competencies within existing clinical and educational contexts, while maintaining established standards of professional patient safety, and undergraduate dental students, educational could in which students first demonstrate independent clinical and evaluate AI-generated clinical and relevant may require more involving diagnostic cases, and critical evaluation of AI applications. practising continuing professional development should address the of AI clinical applicability, protection, and responsible implementation within practice educators represent an and professional development should the critical evaluation of AI the design of appropriate learning and of and and the ethical and implications of AI-supported these educational design should the professional to the required observable learning outcomes, opportunities for supervised practice, and assessment of what can demonstrate independently and with appropriate AI Such alignment may institutions and address potential competency mismatches while an on with individual AI propose this as a educational framework requiring and empirical evaluation, rather than as a of AI integration in dental education educational in this should be interpreted in to their levels of empirical research provides of emerging AI capabilities, variation in curricular and the of clinically professional competencies. However, the proposed double competence gap framework, the assessment of independent and AI-supported clinical competence, and the use of such as represent conceptual or educational approaches whose has not potential contribution to clinical reasoning, AI literacy, and professional should therefore be examined through systematic appropriate and empirical evaluation before broader implementation is of this educational alignment is dental simulation, students may first demonstrate independent clinical and critically AI-supported The provides an of how such learning could be although its educational has not and evaluation would therefore be before broader curricular AI may offer opportunities to support learning and clinical decision-making, its integration into dental education also raises important ethical, and professional These should be potential rather than as implementation concern is students or professionals may in AI-generated potentially independent critical on AI may contribute to increasingly diagnostic or clinical judgement to AI systems before developing Generative AI may also but or clinical information, independent established clinical and relevant concerns patient and when clinical information is into AI and AI tools may perform across patient clinical contexts, or also requires as AI-supported and assessments may independent knowledge and expectations for appropriate AI use and are challenges the of maintaining professional accountability and that AI-supported learning does not opportunities to develop independent clinical competence. also a rationale for the assessment proposed in which students demonstrate clinical both independently and when critically AI-supported this symbiotic intelligence should not be interpreted as an that necessarily educational or clinical it a conceptual in which potential be the of and professional Consequently, AI integration in dental education should through and empirically educational the proposed double competence gap also requires attention to the competencies of dental AI integration cannot solely on technological or individual development should therefore be a of both initial dental education and continuing professional educators need opportunities to develop competencies in critically AI and appropriate AI-supported learning activities, and independent and clinical Such competencies also protection, professional accountability, and relevant institutional governance requirements.We propose that faculty development should structured professional learning, curriculum design, and supervised with AI-supported educational should concerning AI appropriate use of clinical in assessment, and for emerging among dental educators, educational and in AI and data may support responsible faculty development should not be to training in AI It should to informed and professional about and whether AI should be incorporated into dental education. 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Dentistry provides a relevant AI-supported technologies are increasingly in diagnostic assessment, clinical support, patient communication, and professional The how AI-related competencies may be addressed through educational the professional tasks, regulatory requirements, clinical and educational of from of medicine, and other healthcare Consequently, the proposed framework should not be assumed to across professional or national therefore present the double competence gap as a potentially conceptual framework whose applicability, and educational implications require empirical in different professional education the proposed double competence gap framework and its educational The distinguishes between potential mismatches initial professional education and emerging as practising dentists encounter evolving AIrelated competency requirements. AI literacy, within the broader concept of Professional Digital Competence, an important competency while symbiotic intelligence provides a conceptual perspective on responsible The framework also curriculum assessment, faculty development, and continuing professional development as educational Importantly, the is intended to conceptual and future empirical rather than to demonstrate the or of the proposed competence The proposed framework may a basis for educational professional competencies, and in of evolving knowledge from dental practice and emerging AI Such should to that critical, and responsible AI use an integral part of the professional knowledge The increasing relevance of AI in dental practice also raises questions about whether existing professional institutional and continuing professional development arrangements adequately address emerging requirements concerning AI-related competencies, patient safety, data protection, and professional accountability. Rather than that are we suggest that the of existing regulatory and professional frameworks should be systematically as technologies become more widely into dental practice this the potential double competence gap for a educational in which Professional Digital Competence provides a competency AI literacy addresses the specific requirements of critically and AI technologies, and symbiotic intelligence offers a conceptual perspective on responsible these may the development of educational that independent clinical judgement while the critical and appropriate use of AI in patient-centred dental care.The increasing integration of AI into dental practice raises important questions about how professional education can maintain alignment with evolving clinical and technological requirements. In this Opinion article, we have proposed the double competence gap as a conceptual framework for examining potential mismatches in both initial dental education and the continuing professional development of practising these challenges requires more than with AI Dental education should clinical knowledge, independent diagnostic reasoning, ethical and professional accountability while developing the competencies to critically evaluate and appropriately use AI-supported From this symbiotic intelligence offers a conceptual to in which AI may but should not professional expertise and patient-centred clinical propose that curriculum assessment of independent and competence, faculty development, and continuing professional development represent relevant educational such as may opportunities to these approaches through supervised scenario-based simulation, but their educational be established before broader implementation is existence and extent of the proposed competence and the of educational intended to address remain empirical research should therefore how AI-related competencies are assessed, and across different educational and professional contexts. The is not simply to AI but to prepare dental professionals to engage with emerging technologies and in support of patient-centred