Self-assessment of professional confidence among dental students in the era of modern digital technologies: a cross-sectional study

Introduction

Self-assessment plays a crucial role in setting educational goals for dental medicine students. This process should be monitored continuously, as their training encompasses theoretical education, self-directed learning, clinical reasoning and analysis, as well as the development of manual and technical skills. The interplay between these diverse components of dental education often leads to confusion among students when evaluating their own level of preparedness. A new and increasingly influential factor in this context is the emergence of technological innovations and the near-complete digitalization of the dental field.

A significant portion of the dental curriculum focuses on developing students’ technical skills across various disciplines. However, technical competence alone does not suffice to shape a well-rounded dental professional. If students adopt the mindset that technical skills are the sole marker of competence, their confidence may be misleading. Therefore, substantial emphasis should also be placed on cultivating analytical thinking for clinical decision-making. While clinical competencies and skills are commonly evaluated through practical and theoretical exams, it remains debatable whether these assessments truly predict clinical readiness (1).

It is often empirically observed that students’ self-assessments in medicine and dental medicine differ significantly from those of their instructors. A likely reason for this discrepancy is the students’ unconscious failure to evaluate the quality of the outcomes in relation to the effort invested. Research shows that individuals with higher competence tend to self-assess more cautiously and critically, whereas those with lower competence often paradoxically overestimate their abilities—a phenomenon known as the Dunning-Kruger effect (2,3). Cognitive skills encompass conscious intellectual effort, memory synthesis, information processing, knowledge application, intelligence, mathematical reasoning, motor coordination, and problem-solving. In contrast, non-cognitive skills involve less conscious effort and are illustrated through motivation, integrity, responsibility, social aptitude, and interpersonal attitudes (4). This raises concerns about the validity of self-assessment, as it is essential for students to accurately identify and appreciate the value of their cognitive skills. Students frequently confuse these with their ability to substitute them using the potential of artificial intelligence (AI), which is increasingly utilized in dental practice.

By relying on digital tools and AI, students may inadvertently rely on instant solutions that leave little impact on their theoretical and clinical development. The curriculum in Bulgarian dental universities undergoes a stage of fundamental general medical and dental theoretical training, followed by a transition into clinical education. Similar educational systems are implemented in other European countries, where theoretical and practical training are initially conducted sequentially and subsequently in parallel, with the aim of developing the skills necessary for the independent management of comprehensive dental patient care (5). Accumulated theoretical knowledge during undergraduate training is a fundamental prerequisite for seamless clinical performance (6). Such knowledge is essential for examination, diagnosis, and treatment planning. The integration of AI in these processes is highly beneficial for rapid data processing, making diagnostics more efficient, productive, and patient-centered. Applications of AI in orthodontics can generally be classified into two categories: virtual-diagnostic and robotic-therapeutic. The virtual type utilizes mathematical algorithms to extract data from patient records (7-9), while the robotic type performs the physical component of treatment procedures (10).

Students may equate self-confidence in clinical procedures with their future clinical success as dentists. Therefore, encouraging and nurturing confidence would help them develop their own personal development plan (11). The educational process can be significantly enhanced by incorporating self-assessment among dental students (12). Self-assessment is a critical skill that dental practitioners must acquire to deliver competent oral healthcare. Students should monitor and evaluate the quality of their thinking and behavior during training and identify strategies that improve their understanding and clinical performance (13). Moreover, self-assessment should continue throughout their professional careers as practicing dentists (14). Consequently, dental students must learn how to transfer theoretical knowledge into clinical practice.

The cited scientific studies among students from different educational systems attempt to find the balance between confidence and competence of dental students, as this would be crucial for optimizing educational practices and increasing student outcomes.

Because AI is a relatively new trend in this evidence-based discipline, the information on its role in diagnostic and therapeutic processes is still accumulating. To date, the data published in the scientific literature regarding the need to implement AI in everyday dentistry and its role in diagnostic and therapeutic procedures remain inconclusive. Based on our empirical experience and that of other authors, some dentists consider AI an important diagnostic tool that may enhance clinical decision-making, whereas others perceive it as a potentially confusing, unnecessary, and expensive addition to the diagnostic process (15). The lack of sufficient legislative regulation regarding procedures performed by AI and the associated responsibilities should not be underestimated. Another challenge is investing in training doctors in these technologies and the moral dilemma of the purity of the data on which AI is trained and developed. Modern research helps to gather data on the need for structural inclusion of AI in the preparation of current and future dental professionals for the implementation and sustainable adoption of this technology in dental practice (16). Therefore, it would be valuable to investigate the attitudes of future dental practitioners toward the development of digital technologies, as well as their perspectives on the ethical use of and trust in these technologies.

Therefore, it would be valuable to investigate the attitudes of future dental practitioners toward the development of digital technologies, as well as their perspectives on the ethical use of and trust in these technologies.

The aim of this descriptive cross-sectional study is to analyze dental students’ attitudes toward independent diagnostic and clinical decision-making, their self-assessments of educational preparation and professional confidence, their readiness to safely integrate digital systems into clinical practice, and their vision on teledentistry.

The study evaluates the students’ critical self-reflection regarding the knowledge acquired within the Bulgarian educational system, exploring how they self-assess their abilities to synthesize and apply this knowledge in practice. It also assesses, under hypothetical scenarios, their readiness to adopt teledentistry. Ultimately, this research seeks to inform and guide dental curricula to better align with students’ evolving expectations, fostering the confidence needed to navigate complex clinical environments. We present this article in accordance with the SURGE reporting checklist (available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-0091/rc).

Methods

Study design and participants

A cross-sectional study was conducted, collecting data through a questionnaire survey among dental students from a single university. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Faculty of Dental Medicine, Medical University, Sofia, Bulgaria (Approval No. 544). Participation was voluntary, and informed consent was implied through questionnaire completion. No personal or identifiable information was collected.

A structured questionnaire with seven closed-ended questions was developed and administered. Each question was clearly formulated, with sufficient clarification. Each question was single-choice with one selectable option among four predefined answers.

The questionnaire was completed voluntarily and anonymously by all participants. The study targeted fourth- and fifth-year dental students, a selection based on their stage of clinical education. In the second and third years, students’ training in specialized dental disciplines is primarily theoretical and limited to phantom head simulations. In contrast, fourth- and fifth-year students undergo hands-on clinical training, managing real patients under faculty mentorship. To meet curriculum requirements and qualify for their final examinations, these senior students must diagnose and treat a mandatory quota of clinical cases across all dental specialties.

The questionnaire was distributed via Google Forms within closed groups of fourth-year students (n=180) and fifth-year students (n=174). All were invited to complete the questionnaire; a total of 354 students. The condition was that it can only be completed within 1 week. The questionnaire was completed within 1 week by 192 people who were included in the study. Anyone who submitted a completed questionnaire after this deadline was not included in the data analysis. It was not possible for the same addressee to complete the questionnaire twice. It was necessary to complete all questions in the survey in order to reach the end and send it. Only one answer to a question was possible, which excludes the possibility of moving on to another question without answering the previous one and completing the survey. The percentage for assessing the distribution of answers by criteria was estimated on 192 respondents, which equals 100% of the sample. By gender, the percentage of respondents is distributed as follows: 61 men (31.8%) and 131 women (68.2%). Their mean age was 22.93±1.58 years (range: 20–32).

The questionnaire contained seven questions, but in the present study, only questions 1–4 were analyzed. In total, the seven questions describe students’ attitudes in three main areas:

• Their perceived level of theoretical preparedness for making clinical diagnoses and their perceived need for postgraduate specialization—questions 1 and 2;
• Their opinions regarding the ongoing digitalization of dental medicine and their acceptance of teledentistry as a future mode of practice—questions 3 and 4;
• The perception of AI as an assistant or main tool for diagnosis in dentistry—questions 5, 6, and 7.

Data from the final three questions were utilized in a previous study examining the attitudes of dental professionals and patients toward the integration of AI in dentistry (17).

The study focuses on analyzing the four questions (as a subset of elements of the overall study) because the data from them are suitable for developing the task we defined in the introduction. The questions and the available response options for the respondents are provided in Appendix 1, following the main text.

Questionnaire validation

A newly developed questionnaire was designed, peer-reviewed by experts, and pilot-tested.

The questionnaire was designed by two researchers from the study team: a university lecturer directly involved in the students’ theoretical and practical training, and a healthcare management specialist. The questionnaire was reviewed by academic experts in dental education and digital health to ensure content validity. This interdisciplinary collaboration ensured that the questions were clear and accessible to students while remaining focused on emerging trends in dental practice. To establish content validity, the draft questionnaire was subsequently reviewed by academic experts in dental education and digital health. Specifically, two professors evaluated the formulated items: one lecturer from the host university (also a co-author of this study) and one professor of communication and organizational behavior from an external institution.

This reduces concerns about potential bias on the part of the team and the respondents. A pilot test was conducted with a small group of sixth-year students (n=20) who were not included in the closed group in which the final survey was conducted. They received the questionnaire electronically and were requested to complete it within 24 hours. Additionally, they were asked to provide feedback on the clarity of the questions and the appropriateness of the response options, particularly if they encountered any difficulty in selecting an answer. The purpose of the pilot study was to verify and assess the clarity of the questions and the reliability of the study. Based on the feedback, minor revisions were made to improve question structure and interpretability. The pilot project was used only to achieve maximum correctness of the questions asked and to refine the assessment tools. No pilot results were described or reported based on it.

Statistical analysis

In accordance with the analytical framework of the study, descriptive statistics and frequency distributions were applied to the data based on the selected variables. Data were entered and analyzed using IBM SPSS Statistics Version 27.0.1.0, MedCalc Version 19.6.3, and Microsoft Excel (Office 2021).

Results

The members of the closed student groups totaled 354. Of these, a total of 192 students were included in the study (which represents 54% of all fourth- and fifth-year students), of whom 61 (31.8%) were male and 131 (68.2%) were female. Respondents who did not answer all questions or who participated outside the data collection time were not included. The average age of the studied group of students was 22.93±1.58 years, ranging from 20 to 32 years.

Responses to four key questionnaire items were analyzed and grouped into two thematic categories as described in the research methodology. The first category included two questions on students’ theoretical preparedness, confidence in their own knowledge and skills, and views on postgraduate specialization. The second category addressed perceptions of digitalization, including whether it is seen as a primary or auxiliary tool in dental practice, and attitudes toward teledentistry. Following descriptive statistics and frequency distributions, the results were presented graphically.

The first question assessed students’ self-evaluation regarding their theoretical preparedness for making dental diagnoses. The question posed was: “As future dental practitioners, do you consider your theoretical knowledge sufficient for diagnostic purposes, or would you rely on external sources such as search engines?”

The respondents’ answers (n=192) were divided into four predefined options:

• “I would seek a second opinion from a colleague”—selected by 96 (50.0%) of students, representing the most frequent response;
• “I would verify my knowledge using a search engine”—chosen by 57 (29.7%);
• “I would rely entirely on my own knowledge”—selected by 28 (14.6%) of students (approximately one-seventh);
• “I am unsure how I would proceed in such a situation”—indicated by 11 (5.7%) of respondents.

These results are presented with values in Figure 1.

Figure 1 Frequency distribution of responses to the question: “As future dental practitioners, do you consider your theoretical knowledge sufficient for diagnostic purposes, or would you rely on external sources?”

Contemporary dental medicine is becoming increasingly personalized, with treatment approaches tailored to the individual needs of each patient. This trend demands greater specialization and deeper knowledge within specific disciplines on the part of dental practitioners. However, in real-world clinical settings, this level of specialization is not always feasible, and specialized procedures are often performed by general dentists without formal postgraduate training.

To explore students’ perspectives on this issue, the second questionnaire item asked: “Do you believe that specialization is mandatory in order to practice specialized fields of dentistry (such as orthodontics, oral surgery, implantology, etc.)?”

Student responses (n=192) were distributed as follows and are presented in Figure 2:

Figure 2 Frequency distribution of dental students’ opinions on the need for specialization in order to practice specialized fields of dental medicine (e.g., orthodontics, oral surgery, implantology).

• “Yes, specialization is mandatory”—89 (46.4%) of respondents;
• “It is possible with a short course”—77 (40.1%) of respondents;
• “I am not sure”—22 (11.5%) of respondents;
• “I believe I can manage all procedures without specialization”—4 (2.1%) of respondents.

These findings indicate that nearly half of the students recognize the importance of pursuing postgraduate education in order to practice specialized dental disciplines. However, a significant proportion of future practitioners believe that brief courses may be sufficient, which highlights a potential gap in understanding the complexity and clinical responsibility required in specialty care.

Digital technologies enable faster data acquisition from clinical and diagnostic examinations. Technologies rapidly entering modern dentistry include 3D digital models generated via intraoral scanning, digital radiography, and extra- and intraoral photography. The data generated by these modalities are increasingly processed using AI, which optimizes clinical time, assists in establishing accurate diagnoses, and aids in formulating comprehensive treatment plans. The introduction of digital diagnostic methods into dental medicine—particularly in the diagnostic phase—requires clinicians to be capable of accurately interpreting the data generated. This implies that dentists must have a strong foundational understanding of conventional diagnostic techniques and the necessary knowledge to formulate a final diagnosis, while also integrating insights obtained from digital analysis. This digitization of clinical workflows, beginning with diagnosis, is supported by nearly all surveyed students. The majority of respondents, 137 (71.4%) out of a total of 192 students who participated, expressed a preference for digital diagnostic methods to be taught alongside conventional approaches. Slightly over one-quarter, 51 (26.6%), believed that traditional principles should be taught first. Only 3 (1.6%) of respondents supported completely replacing conventional methods, while 1 (0.5%) was undecided. These results are presented in Figure 3.

Figure 3 Frequency distribution of participants’ opinions on the need to include digital diagnostic methods in dental education.

Teledentistry leverages modern communication tools to support patient-provider interaction, allowing for the exchange of clinical information regarding diagnosis, treatment monitoring (especially in orthodontics) or referral and recommendations to the patient. To assess students’ attitudes toward teledentistry, respondents were asked the following question: “What is your opinion on dental teledentistry (e.g., a patient enters a self-service booth, scans their oral cavity, and the data is sent to a digital processing center; you then receive the data by email and develop a treatment plan)?”

The analyzed results (Figure 4) revealed that 48 (25.0%) of students (n=192) expressed trust in teledentistry. Among them, 25 (13.0%) cited time efficiency as the main advantage, while 23 (12.0%) indicated a genuine belief in technological advancement as their motivation. However, more than twice as many students, 102 (53.1%), responded: “I am not yet ready to diagnose and treat via teledentistry.” Additionally, 42 (21.9%) of students reported being unsure of their stance on the issue, indicating that a notable proportion of respondents had not formed a clear opinion about teledentistry.

Figure 4 Frequency distribution of the perception of the surveyed students of the introduction of teledentistry into practice.

Discussion

Dental medicine requires highly specialized academic training, supported by ongoing knowledge acquisition throughout a dentist’s professional career. These growing competencies are applied in a complex clinical environment shaped by human emotions, diverse patient psychological profiles, stress, and time constraints. Consequently, dental professionals must develop fine psychomotor skills, rapid cognitive processing, and analytical thinking based on a substantial theoretical foundation. This can only be achieved through continuous advancement in both theoretical knowledge and clinical practice. These competencies cannot be entirely replaced by AI or fully automated systems.

Nevertheless, familiarity with digital tools and their appropriate integration into daily practice can significantly reduce time consumption and human error, while also enhancing the clinical experience for patients. This raises the question of where to draw the line between beneficial and excessive reliance on digital technologies and AI-based diagnostic systems—and their impact on traditionally manual aspects of dental care. The value of conventional dental education, theoretical knowledge acquisition, analytical thinking, and manual skill development must be preserved. Dental students need to become more confident as their education progresses, so the survey was conducted among fourth- and fifth-year students. In Bulgaria, fourth- and fifth-year dental students practice in well-equipped clinical environments, treating actual patients who present with diverse dental conditions and varying levels of anxiety. Under the guidance of clinical instructors, students learn to address these individual patient concerns by combining faculty supervision with the preclinical experience and technical skills previously acquired through work on typodont models.

It is commonly believed that repeated practice of a procedure improves manual dexterity and increases confidence. At the same time, the trend in dentistry is towards more personalized approaches and treatment methods. Therefore, future dentists must have self-confidence in understanding individual patient needs, diagnostic assessments, and clinical treatment plans. Therefore, the study focuses on the personal assessment of the students’ own confidence in their preparation to become dentists. Findings from this study revealed that only 14.6% of students at the dental faculty where the study was conducted reported confidence in their preparation for working in a real clinical environment. A substantial portion expressed willingness to rely on colleagues’ opinions, while nearly one-third stated they would verify information via online search engines. The high percentage of students who preferred to consult a colleague or refer to an external digital resource likely reflects professional caution and a desire for collaborative clinical reasoning, rather than insufficient knowledge or a lack of confidence. These findings suggest that educators should focus on fostering independent decision-making in students, encouraging them to synthesize their theoretical knowledge with supporting AI-generated data. One major challenge in applying AI in clinical dentistry is data standardization, which is crucial for machine learning. The broader and more representative the dataset, the more accurate the AI-generated outputs become (8,18-20). Gao et al. highlight critical challenges related to full trust in AI systems, including inaccurate data annotation, limited feature recognition, lack of universally applicable models, potential algorithmic bias, legal liability, and concerns over patient data confidentiality (21). It is very likely that for this reason, students still do not fully understand the burden of clinical responsibility and are more inclined to trust externally processed diagnostic data. This would explain why a significant percentage of these students strongly trusted teledentistry, viewing it solely as a mechanism for analyzing diagnostic data without real clinical accountability. On the other hand, the massive amount of dental health data is increasingly overwhelming for clinicians and often necessitates the integration of AI solutions (22,23). Dental professionals must be capable of interpreting, justifying, and understanding AI-generated outputs (24). AI should be regarded as a powerful aid—but one that must be introduced in a controlled and ethical manner.

The coronavirus disease 2019 (COVID-19) pandemic brought teledentistry to the forefront (25). In dentistry, teledentistry enables practitioners to offer remote consultations and virtual follow-ups, extending access to care and creating new avenues for patient outreach, including marketing of in-office treatments. However, dental students must understand that in real-world settings, dentists do not rely solely on data; in-person interaction with patients provides essential contextual information for accurate diagnoses. A growing body of research has focused on assessing dental professionals’ attitudes toward and awareness of teledentistry (26-29). These studies consistently report increasing support for teledentistry as a viable modality for remote diagnosis, patient monitoring, and treatment planning. This approach encompasses various methods of remote communication, enabling practitioners to review clinical data, monitor disease progression, and track treatment outcomes.

Therefore, clinical training and direct experience working with a real patient remain a critical priority in dental education. This is the model of education in the educational institution we studied. Students must learn to gather and interpret patient information—even in emotionally charged situations—and integrate it with digital data. Classical diagnostic and treatment methods cannot be bypassed in favor of purely digital clinical workflows. The dentist needs to develop competencies in both domains and apply the best of both in everyday practice.

The results presented reflect the self-assessments and personal perceptions of students from a single educational institution. Therefore, this study cannot provide a complete picture of dental training more broadly, but can guide towards areas where students are unsure. These problems sparked the team’s interest and deserve to be more thoroughly researched and analyzed. The data obtained from the descriptive analysis can be used by other educational systems when testing and comparing different models.

Another result of the study shows that the majority of students—137 (71.4%) out of a total of 192—prefer digital diagnostic methods to be taught alongside conventional approaches. Significant gaps in digital dental competence have been identified in both students and practicing dentists (17). It is most logical that this should be addressed early in student training. Modernizing dental education requires integrating digital technologies into clinical dentistry training. This trend has also been observed in other studies in other educational systems. For example, Dhawan et al. (29) found that over half of the respondents they surveyed (63.7%) preferred to use a combination of digital and conventional methods in their clinical practice. Therefore, the path to change in dental education is towards a systematic and proper integration of digital technologies into the curriculum in combination with the development of students’ theoretical and manual competences.

Specialization in dentistry is associated with increased efficiency and improved clinical outcomes (30). Postgraduate dental education plays a key role in preparing practitioners for advanced care. However, for recent graduates, choosing a career path and area of specialization can be difficult. These choices should reflect both personal interest and public health needs. There is a notable disparity in dental care access between urban and rural areas. In remote regions, general practitioners may be required to perform a broader range of services, whereas in urban settings, specialized care is more accessible and preferred. Certain specialties—such as orthodontics, pediatric dentistry, oral surgery, and maxillofacial surgery—are in particularly high demand due to their complexity and growing clinical need. These fields are often associated with higher earnings and job security. Training in these areas typically involves 3 to 4 years of structured residency or doctoral programs, ending with formal examination and certification. In contrast, short-term courses or workshops—while beneficial—do not provide the same level of competence or assurance. Certificates from such courses usually confirm attendance but not proficiency. Importantly, specialists must continue learning even after certification. In our survey, 89 (46.4%) responded that they perceived the need for specialization as mandatory. Therefore, the attitudes of the majority of students included in the study are directed towards continuing their dental education. Shin et al. (31) also reported a high percentage of graduating students interested in pursuing postgraduate studies. Based on this, they believe that dental universities need to increase their mentoring efforts to help students make career decisions based on personal interest in the specialty, which will bring them satisfying results in the long term. Almohefer et al. (32) reported that 49.5% of respondents stated that they planned to specialize in a specific dental field in the coming years. This trend is promising and should be encouraged, because the reason may be a personally conscious decision of the students or it may be momentarily influenced by their teachers and supervisors. The subjective assessment of the student for the acquired abilities and confidence to perform tasks and achieve goals should also correlate in their assessment of self-efficacy. Those who realize it become more critical and set themselves higher professional goals and are ready to deal with challenges. In dental students, confidence largely depends on the quality of their education and fulfilled clinical requirements. For the proper orientation of students and their motivation for future specialization, it would be useful for the university to organize optional additional short-term internships in specialized clinics.

Dentistry is a fast-evolving discipline, and technological advancement continues to reshape the field. This requires a commitment to lifelong learning among dental teams. Quality dental care is grounded in evidence-based medicine. Maintaining this standard requires the development of clinical competence, adherence to professional ethics, and sustained motivation. Future dental professionals realize that it is necessary to commit to lifelong learning, integrating technological advancements with their foundational knowledge and clinical capabilities. Specialization enhances the precision of diagnosis, treatment planning, and patient care. It expands clinical reasoning and enables management of complex cases. The formation of a skilled professional is not solely the responsibility of academic instructors—it is equally a personal endeavor. A competent dentist must be inquisitive, self-directed, and engaged in regular self-assessment to ensure the effectiveness of their treatments and continued growth as a practitioner.

Limitations of the study

The study was conducted among students from a single university and does not provide a complete picture of dental education in different dental schools, training systems, or levels of digital exposure. The study focused on the attitudes and self-rated confidence of future dentists. The study did not use instruments to objectively assess theoretical knowledge, competence, or ethical practice. Therefore, it is impossible to formulate conclusions about the level of professional competence or educational deficits. The results only show the students’ self-assessment of their confidence in their own preparation and their beliefs about the need for continuing education.

Conclusions

The study aimed to identify the self-assessment of students in a specific institution. It was found that dental students have insufficient confidence in their preparation, which can make them feel insecure in a stressful clinical situation. The results showed that students have a tendency to prefer hybrid diagnostic methods (a combination of AI-based diagnostics and personal interpretation), which probably gives them greater confidence in their practice. Their practical thinking allows them to perceive positively the introduction of teledentistry, which would save them additional clinical time.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the SURGE reporting checklist. Available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-0091/rc

Data Sharing Statement: Available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-0091/dss

Peer Review File: Available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-0091/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-0091/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Faculty of Dental Medicine, Medical University, Sofia, Bulgaria (Approval No. 544). Participation was voluntary, and informed consent was implied through questionnaire completion. No personal or identifiable information was collected.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Wanigasooriya N. Student self-assessment of essential skills in dental surgery. Br Dent J 2004;197:11-4. [Crossref] [PubMed]
2. Goob J, Erdelt K, Nold E, et al. Correlation between theoretical knowledge and the difference between self- and teaching doctor-assessment. J Dent Educ 2024;88:411-7. [Crossref] [PubMed]
3. Kruger J, Dunning D. Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments. J Pers Soc Psychol 1999;77:1121-34. [Crossref] [PubMed]
4. Ramlogan S, Raman V. An educational approach for early student self-assessment in clinical periodontology. BMC Med Educ 2022;22:33. [Crossref] [PubMed]
5. Brundin M, Sjöström M. Student confidence in clinical competence during dental education. BDJ Open 2024;10:92. [Crossref] [PubMed]
6. Mittal P, Jadhav GR, Pawar M, et al. Impact of self-assessment on dental student's performance in pre-clinical conservative dentistry course. BMC Oral Health 2024;24:593. [Crossref] [PubMed]
7. Ghaffari M, Zhu Y, Shrestha A. A review of advancements of artificial intelligence in dentistry. Dent Rev 2024;4:100081.
8. Emiliyanov E, Kostov I. Capabilities and limitations of artificial intelligence in cephalometric analysis. MedInform 2025;12:2002-8.
9. Ivanišević A, Tadin A. Artificial Intelligence and Modern Technology in Dentistry: Attitudes, Knowledge, Use, and Barriers Among Dentists in Croatia-A Survey-Based Study. Clin Pract 2024;14:2623-36. [Crossref] [PubMed]
10. Yordanova G, Chalyovski M, Gurgurova G, et al. Digital design of laser-sintered metal-printed dento-alveolar anchorage supporting orthodontic treatment. Appl Sci (Basel) 2023;13:7353.
11. Meisha DE, Al-Dabbagh RA. Self-confidence as a predictor of senior dental student academic success. J Dent Educ 2021;85:1497-503. [Crossref] [PubMed]
12. Kirov D, Kazakova S, Kirilova J. Students’ self-assessment in preclinical and clinical education of prosthetic dentistry. J IMAB 2014;20:575-7.
13. Iguchi A, Hasegawa Y, Fujii K. Student Potential for Self-assessment in a Clinical Dentistry Practical Training Course on Communication Skills. Med Sci Educ 2020;30:1503-13. [Crossref] [PubMed]
14. Dang HT, Tran TA, Vo Truong NN, et al. Knowledge and Attitudes of Dental Students on E-portfolios as a Tool for Learning, Assessment, and Professional Development in Dental Education. Cureus 2025;17:e81980. [Crossref] [PubMed]
15. Roganović J, Radenković M, Miličić B. Responsible Use of Artificial Intelligence in Dentistry: Survey on Dentists' and Final-Year Undergraduates' Perspectives. Healthcare (Basel) 2023;11:1480. [Crossref] [PubMed]
16. Goey RS, Elenbaas L, Berkhout E, et al. AI Acceptability in Dentistry: Insights from Dental Professionals and Students in the Netherlands: A Pilot Study. Int Dent J 2025;75:103933. [Crossref] [PubMed]
17. Kostov II, Yordanova GR. Attitudes of dentists and patients towards the introduction of artificial intelligence in dentistry. J Med Life 2025;18:472-7. [Crossref] [PubMed]
18. Sitaras S, Tsolakis IA, Gelsini M, et al. Applications of Artificial Intelligence in Dental Medicine: A Critical Review. Int Dent J 2025;75:474-86. [Crossref] [PubMed]
19. Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res 2020;99:769-74. [Crossref] [PubMed]
20. Feher B, Tussie C, Giannobile WV. Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches. Front Artif Intell 2024;7:1427517. [Crossref] [PubMed]
21. Gao S, Wang X, Xia Z, et al. Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications. Med Sci Monit 2025;31:e946676. [Crossref] [PubMed]
22. Hołyst JA, Mayr P, Thelwall M, et al. Protect our environment from information overload. Nat Hum Behav 2024;8:402-3. [Crossref] [PubMed]
23. Kwiatek J, Leśna M, Piskórz W, et al. Comparison of the Diagnostic Accuracy of an AI-Based System for Dental Caries Detection and Clinical Evaluation Conducted by Dentists. J Clin Med 2025;14:1566. [Crossref] [PubMed]
24. Bonny T, Al Nassan W, Obaideen K, et al. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023;12:1179. [Crossref] [PubMed]
25. Deshpande S, Patil D, Dhokar A, et al. Teledentistry: A Boon Amidst COVID-19 Lockdown-A Narrative Review. Int J Telemed Appl 2021;2021:8859746. [Crossref] [PubMed]
26. Moosa Y, Owasitth R, Samaranayake LP, et al. Thai Dental Students' Perspectives on Teledentistry: A Cross-Sectional Analysis. Eur J Dent 2026; [Epub ahead of print]. [Crossref] [PubMed]
27. Arafa A, AbdelShakoor N, Hegazy A, et al. Dental professionals' knowledge and awareness of teledentistry. BMC Oral Health 2026;26:1058. [Crossref] [PubMed]
28. Hall MA, Mahmoud AZ, Mohamed OS, et al. Knowledge, Awareness, and Perception of Dental Students Regarding Digital Dentistry in Egypt: A Cross-Sectional Study. Cureus 2024;16:e71061. [Crossref] [PubMed]
29. Dhawan P, Kaur H, Dogra S, et al. Current State of Digital Dentistry in Clinical and Contemporary Dental Education - A Prospective Study. Indian J Dent Res 2025;36:398-401. [Crossref] [PubMed]
30. Alshahrani S, Masud N, Alswayyed T, et al. Emerging trends in dental specialty and employment choice among male dentists graduating from King Saud University between 2005 and 2015. Egypt J Hosp Med 2018;70:948-54.
31. Shin JH, Kinnunen TH, Zarchy M, et al. Factors influencing dental students' specialty choice: a survey of ten graduating classes at one institution. J Dent Educ 2015;79:369-77. [PubMed]
32. Almohefer SA, Alkattan RK, Aldakhayel GD, et al. Perception and confidence levels of dental students and newly graduate dentists during prosthodontic procedures. Open Dent J 2025;19:e18742106361704.