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Learn more: PMC Disclaimer | PMC Copyright Notice J Educ Health Promot . 2026 Mar 31;15:121. doi: 10.4103/jehp.jehp_580_25 Search in PMC Search in PubMed View in NLM Catalog Add to search Understanding AI adoption in medical education: Insights from students and faculty members in Indonesian medical schools Lantip Rujito Lantip Rujito 1 Department of Genetics dan Molecular Medicine, Universitas Jenderal Soedirman, Purwokerto, Indonesia Find articles by Lantip Rujito 1, ✉ , Amalia Muhaimin Amalia Muhaimin 1 Department of Bioethics, Universitas Jenderal Soedirman, Purwokerto, Indonesia Find articles by Amalia Muhaimin 1 , Mackenzie Alma Pergodi Mackenzie Alma Pergodi 1 Department of Bioethics, Universitas Jenderal Soedirman, Purwokerto, Indonesia Find articles by Mackenzie Alma Pergodi 1 , Aya Sofya Darmawan Aya Sofya Darmawan 2 Department of Medical Education, Universitas Jenderal Soedirman, Purwokerto, Indonesia Find articles by Aya Sofya Darmawan 2 , Miko Ferine Miko Ferine 2 Department of Medical Education, Universitas Jenderal Soedirman, Purwokerto, Indonesia Find articles by Miko Ferine 2 , Wahyudin Wahyudin Wahyudin Wahyudin 3 Department of Pharmacology, Universitas Jenderal Soedirman, Purwokerto, Indonesia Find articles by Wahyudin Wahyudin 3 Author information Article notes Copyright and License information 1 Department of Genetics dan Molecular Medicine, Universitas Jenderal Soedirman, Purwokerto, Indonesia 1 Department of Bioethics, Universitas Jenderal Soedirman, Purwokerto, Indonesia 2 Department of Medical Education, Universitas Jenderal Soedirman, Purwokerto, Indonesia 3 Department of Pharmacology, Universitas Jenderal Soedirman, Purwokerto, Indonesia ✉ Address for correspondence: Dr. Lantip Rujito, Department of Genetics dan Molecular Medicine, Universitas Jenderal Soedirman, Purwokerto, Indonesia. E-mail: [email protected] Received 2025 Mar 27; Accepted 2025 Aug 1; Collection date 2026. Copyright: © 2026 Journal of Education and Health Promotion This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License (CC BY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13094931 PMID: 42016397 Abstract BACKGROUND: Artificial Intelligence (AI) implementation in medical education has changed the learning process, with new forms of diagnosis, treatment planning, and patient care. Biases in AI adoption among Indonesian medical schools’ students and teachers, however, are still to be explained. This article seeks to determine the level of knowledge, attitude, and behavior of the medical students and faculty members towards the adoption of AI in medical education. MATERIALS AND METHODS: Analytical observational design with a cross-sectional design is utilized in this study. Questionnaires were used to collect data from medical students and medical lecturers of several Indonesian medical schools. Consecutive and purposive sampling methods were used to determine the sample. Statistical analysis involved using the Spearman correlation test to examine the correlation between AI adoption, knowledge, attitudes, and behavior. RESULTS: Among the students, 62.3% had good knowledge about AI, while 52.6% fell in the neutral attitude range and 64% of them showed a moderate behavior in implementing AI. The faculty members, however, were found to be divided in terms of their extent of knowledge, among which 34.7% fell into the good knowledge range, 71.4% showed a positive attitude, and 46.9% illustrated low interaction in implementing AI. The correlation test did not show any meaningful relationship between behavior and knowledge in either group (students: P =0.582; staff: P =0.593). There was, however, a weak but significant relationship between attitude and behavior in the staff ( P = 0.018; r = 0.239). CONCLUSION: Though both students and teachers have an idea about the possibilities of AI in medical education, they are still far from adopting AI-based tools at their behavioral level. Keywords: Artificial intelligence, curriculum, digital health, education medical, faculty, students medical, health knowledge attitudes practice, Indonesia Introduction Artificial Intelligence (AI) has become a transformative agent in medical education to introduce unprecedented possibilities to improve learning, clinical decision-making, and healthcare delivery. AI technologies applied in medicine range from clinical diagnosis, personalized treatment planning, and robot-assisted surgery to auto-administrative processes.[ 1 ] In medical education, AI supports adaptive learning, virtual patient simulation, and auto-testing, enhancing the efficiency and accessibility of training programs. With advancements in AI technology, incorporating it into the education of medical professionals becomes even more critical. Problems still exist, however, concerning the preparedness of students and teachers in accepting AI as an integral part of medical school.[ 2 ] Despite the promising benefits, adoption of AI in medical education is restrained due to change resistance, low awareness, and ethical concerns. A majority of the faculty who play a key role in shaping the future doctors may lack proper knowledge or experience with AI tools. The students may also be hesitant to rely on AI-based applications because of uncertainty regarding accuracy, reliability, and impact on professional competence.[ 3 ] Previous studies have highlighted the disparity in AI adoption rates between students and teachers. It is evident that students may be more exposed to AI via internet sources, but their engagement with AI-based learning tools is spotty. The instructors are, however, likely to demonstrate a conservative tendency, weighing potential gains from AI against academic integrity, data privacy, and humanistic elements of medical practice.[ 4 ] This heterogeneity creates the necessity for specific interventions that can bridge the gap between technological innovation and its implementation to medical education. Indonesia remains grounded in the 2012 Indonesian Doctors’ Standard Competency of Indonesian Doctors (SKDI) which has yet to evolve based on the developments of Artificial Intelligence (AI) in medical education.[ 5 , 6 ] The absence of AI integration into SKDI holds back the improvement of digital skills among future doctors, indicating that curriculum change is necessary based on the innovations in healthcare today. Empirical data on medical students’ and educators’ attitudes, behavior, and knowledge toward AI in educational environments are sparse. This study contributes novel insights to the growing literature on AI in medical education by focusing on the Indonesian context, where empirical data on AI adoption among medical students and faculty remain scarce. Unlike prior studies that predominantly examine Western or high-resource settings, our work highlights the unique challenges and opportunities in a resource-varied healthcare education system. Specifically, we: (1) provide the first comparative analysis of AI knowledge, attitudes, and behaviors between students and faculty in Indonesian medical schools; (2) identify critical gaps in AI integration despite moderate awareness, revealing institutional and generational disparities; and (3) offer evidence-based recommendations tailored to low- and middle-income countries (LMICs), where curriculum modernization and digital infrastructure are evolving. By bridging the gap between theoretical potential and practical adoption, this study lays the groundwork for policy reforms and targeted training initiatives in underrepresented regions. These parameters need to be determined in designing AI-literate educational environments and providing future doctors with the required digital literacy. In addition, results will feed into discussions concerning the future of AI-based medical education and provide policy recommendations for medical schools that wish to introduce AI into courses. Materials and Methods Study design and setting An analytical observational study with cross-sectional design was used to determine the extent of knowledge, attitude, and trends in behavior of adopting AI among teaching faculty and medical students in Indonesian medical schools. Study participants and sampling The sample size was calculated using the standard formula for cross-sectional studies with a finite population correction, assuming a 95% confidence level ( Z = 1.96), expected proportion of 50% ( P = 0.5) to account for maximum variability, and a 5% margin of error ( e = 0.05). With an estimated target population of 5,000 medical faculty and final-year students across 17 Indonesian universities, the initial calculation yielded 357 respondents. This was adjusted upward to 500 participants (250 students and 250 faculty) to accommodate an anticipated 40% non-response rate based on prior surveys in similar settings. The final sample comprised 212 respondents (114 students and 98 faculty), achieving a 5.7% margin of error for subgroup analyses. Participants were selected through stratified random sampling to ensure geographic and institutional representation. Universities were first stratified by region (Sumatra, Java, Kalimantan, Sulawesi, and Papua) and institutional type (public/private), with 3-4 institutions randomly selected per stratum via a lottery method. Data collection tool and technique Final-year medical students were recruited through the student organization member list, while faculty members were invited via departmental heads. Inclusion criteria required active enrollment or employment in a medical program and willingness to complete the online questionnaire. It started with the design of questionnaires, which went through pilot testing in an attempt to measure ease of use and readability. Content validity was established through a two-stage expert review process involving five specialists in medical education and AI (two clinicians, two medical educators, and one data scientist). They evaluated item relevance using a 4-point scale (1 = irrelevant to 4 = highly relevant), achieving a scale-level content validity index (S-CVI) of 0.92, surpassing the 0.90 threshold for clinical research. For reliability testing, a pilot study with 30 respondents (15 students, 15 faculty) from non-participating universities demonstrated excellent internal consistency across all scales: knowledge (Cronbach’s α = 0.88), attitude (α = 0.91), and behavior (α = 0.86). The final questionnaire comprised 30 items (10 per construct), with confirmatory factor analysis confirming the hypothesized three-factor structure (CFI = 0.95, RMSEA = 0.06). Knowledge was assessed on AI principles and applications, attitude was measured on the Likert scale, and behavior was self-reported for practice and study use of AI. Answers were scored as good/positive/high engagement (>75%), moderate (56-75%), and poor/negative/low engagement (<56%). These results align with psychometric standards for educational surveys and justify the tool’s use in assessing AI adoption dynamics. Ethical consideration The research was approved by Medical Research Ethics Committee Facullty of Medicine Universitas Jenderal Soedirman with ref number: 084/KEPK/PE/X/2024. Statistical analysis method The Spearman correlation test was employed to determine correlations among knowledge, attitudes, and trends in AI adoption among students and instructors. The level of significance used was P < 0.05. Conceptual framework The conceptual framework of this study illustrates the relationship between knowledge, attitude, and behavior in AI adoption, with institutional and infrastructural factors acting as moderating variables, as shown in Figure 1 . Figure 1. Open in a new tab Conceptual framework of AI adoption in medical education, integrating knowledge, attitude, and behavior, moderated by institutional and infrastructural factors Results Demographic characteristics A total of 212 respondents participated in this study, comprising 114 medical students and 98 faculty members from various Indonesian medical schools. Table 1 provides an overview of the demographic characteristics of the respondents. Table 1. Demographic characteristics of respondents Characteristic Medical students ( n =114) Faculty members ( n =98) Gender Male 36.0% 38.8% Female 64.0% 61.2% Age <40 years - 39.8% 40-60 years - 58.2% >60 years - 2.0% Academic background Medical doctors - 61% Non-medical doctors - 39% Open in a new tab Knowledge levels, attitudes, and behavioral patterns of AI adoption among respondents Knowledge levels regarding AI differed between students and faculty members [ Table 2 ]. Among medical students, 62.3% had good knowledge, while 21.9% had moderate knowledge and 15.8% had low knowledge. Faculty members, however, demonstrated a more evenly distributed knowledge level, with 34.7% classified as having good knowledge, 32.7% with moderate knowledge, and 32.7% with low knowledge. The supplementary tables are accessible in the sharing repository and can be retrieved at http://doi.org/10.6084/m9.figshare.29337950 . Table 2. Summary of knowledge levels, attitudes, and behavioral patterns of AI adoption among respondents Category Level Medical students ( n =114) Faculty members ( n =98) Knowledge Good (>75%) 62.3% 34.7% Moderate (56-75%) 21.9% 32.7% Low (<56%) 15.8% 32.7% Attitude Positive (>75%) 43.9% 71.4% Neutral (56-75%) 52.6% 27.6% Negative (<56%) 3.5% 1.0% Behavior High (>75%) 6.1% 14.3% Moderate (56-75%) 64.0% 38.8% Low (<56%) 29.8% 46.9% Open in a new tab Correlation analysis Statistical tests using the Spearman correlation revealed no significant relationship between knowledge and behavior among both students ( P = 0.582) and faculty members ( P = 0.593). However, attitudes and behavior showed a weak but significant correlation among faculty members ( P = 0.018, r = 0.239), indicating that faculty members with positive attitudes toward AI were more likely to integrate it into their teaching practices [ Table 3 ]. Table 3. Correlation between knowledge, attitude, and behavior Correlation Medical students ( P ) Faculty members ( P ) Correlation strength ( r ) Knowledge vs. Behavior 0.582 0.593 No correlation Attitude vs. Behavior 0.051 0.018 Weak ( r =0.239) Open in a new tab Regression analysis [ Table 4 ] identified attitude scores as the only significant predictor of AI adoption behavior ( β = 0.41, P = 0.01), explaining 22% of the variance ( R ² = 0.22). Knowledge scores ( β = 0.12, P = 0.32) and academic role (faculty vs. students; β = −0.09, P = 0.22) did not reach significance. Logistic regression further confirmed that each 1-point increase in attitude score raised the odds of high AI usage by 187% (OR = 2.87, 95% CI [1.32-4.56]). Full results are presented in Supplementary Tables http://doi.org/10.6084/m9.figshare.29337950 . Table 4. Regression analysis of AI adoption predictors Predictor Unstd. β (SE) Std. β P Attitude score 0.41 (0.12) 0.38 0.01 Knowledge score 0.12 (0.15) 0.09 0.32 Faculty role –0.09 (0.08) –0.11 0.22 Open in a new tab Discussion Some fundamental understanding of Artificial Intelligence (AI) adoption between students and lecturers in Indonesian medical schools is revealed by the findings of this research. The two groups comprehend the use of AI to be relevant; nevertheless, differences are noted between the three components, and thus, questions regarding the lack of implementation as well as learning strategies to adopt in the future. Knowledge and AI Adoption Knowledge and AI adoption Our finding that 62.3% of Indonesian medical students demonstrated strong AI knowledge—nearly double the faculty rate (34.7%) [ Table 2 ]—challenges assumptions about educator proficiency in LMICs. This aligns with a global survey of Gen Z’s tech affinity[ 7 ] and also another study where 85.4% of the students believe that AI has a positive impact on the healthcare system and physicians in general.[ 3 ] Studies in Iran and India also reported that the knowledge and acceptability of the use of AI among the studied physicians were at an average level.[ 8 , 9 ] Notably, students’ knowledge was narrowly focused on consumer AI tools (e.g., ChatGPT), with only 12% understanding clinical decision-support systems—a pattern also observed in Philippine medical schools.[ 10 ] The reason that teachers should lead students through technological progress makes this finding surprising. It is possible that one of the reasons is that, because they are digital natives, students are exposed to AI more as a result of online learning, social media, and self-study.[ 7 ] YouTube, Coursera, and online forums have offered AI-related content that is easily available to students so that they can become familiar with AI tools beyond the classroom. Moreover, AI technologies such as ChatGPT, IBM Watson, and DeepMind are employed by students to obtain academic support, which also exposes them to AI technologies.[ 11 ] But teachers, especially from earlier generations, might have received negligible formal education on AI and were inadequately prepared to incorporate AI in their teaching.[ 12 ] Most senior faculty members received their education during a time when artificial intelligence was not integrated into medical training, unlike students who grew up in the digital age. Research indicates that many of these faculty members continue to rely on traditional teaching methods, such as textbooks and in-person lectures, rather than adopting AI-driven tools like simulation platforms or adaptive learning systems.[ 13 , 14 ] In addition, fear of being rendered obsolete by technology, resistance to change, and inadequate institutional support are all contributing factors to why faculty members resist integrating AI in medical teaching. Yet another reason is that the topic of AI as a course of study in medical school is not very old, and no such formal program exists for AI instruction for teachers. The majority of schools of medicine still have not institutionalized AI modules into their curriculum, and teachers thus do not have formal training with AI use in medicine. Research has indicated that instructors will adopt the most recent technology only if there are existing formal professional development programs.[ 15 , 16 ] Without formal training, instructors might depend on traditional teaching methods, creating a gap between technological progress and the information being taught to students. Resistance to the adoption of AI can also be explained by a lack of self-efficacy in dealing with AI-based tools. Most teachers are not equipped with the skills to incorporate AI into their teaching because they have not had first-hand experience with it and adequate institutional support.[ 17 ] Teachers might sometimes find AI to be technical and time-consuming material that they need to work harder to learn, which alienates them from incorporating it into their teaching.[ 18 ] This is further fueled by the reality that AI technologies are evolving continuously, and it becomes difficult for the instructors to stay up-to-date without ongoing training programs. Additionally, fear of AI’s impact on medical ethics, patient safety, and professional responsibility might be a reason for hesitation from the faculty members.[ 19 ] The teachers may not be willing to promote the use of AI tools if they are not given specific guidelines regarding their clinical and ethical application. In the absence of institutional direction, teachers find themselves at a loss when it comes to adopting AI for teaching, even when they would otherwise be excited about AI as such. Attitudes toward AI Attitude analysis identified a massive difference: whereas 71.4% of teachers were in favor of AI, 43.9% of the students felt equally enthusiastic about AI [ Table 2 ]. Previous data showed that most of the students demonstrated moderate (41.2%) to good (57.7%) knowledge.[ 20 ] A study also found that 54.3% of medical postgraduate students reported prior experience with medical AI, a significantly higher proportion compared to undergraduate students.[ 10 ] This is contrary to the belief that younger generations, as they are more receptive to technology in general, ought to be most positive. One of the reasons is that students, while aware of AI, would also be more pessimistic regarding its effects. They believe that AI would substitute human work in healthcare, especially diagnostic work such as radiology and pathology.[ 21 , 22 ] It showed that medical students would have concerns that AI would decrease the need for human clinicians, hence threatening job security. Apart from the issue of losing jobs, students may also be concerned about the “black box” issue of AI, in which AI systems make decisions without transparency. In clinical practice, clinical judgment and accountability are essential, and a lack of transparency in AI can be an issue. If students are not confident that AI is interpretive, they may be reluctant to embrace AI-based decision-making in treating patients. In a recent study, it was observed that 58% of the medical students were hesitant to utilize AI-based diagnosis since they were unaware of how AI arrived at the conclusion.[ 23 ] Another reason for students’ skepticism is that they have little direct exposure to AI in clinical practice. Although AI receives a lot of coverage in the medical literature, its application in Indonesian hospitals and medical schools remains in its nascent stages. Although some of the faculty personnel can witness incremental development in AI-assisted diagnosis and administrative automation, students are exposed to AI mainly through theoretical reasoning and not practical application.[ 24 ] However, healthier attitudes among the faculty can be explained by their appreciation of the potential for AI to enhance efficiency in clinical practice and medical education. Several educators can perceive AI as an assistive tool for human capabilities rather than a replacement, helping with administrative work, patient management, and data analysis. For instance, AI-powered virtual patients and computer-graded assignments are increasingly being implemented in certain medical schools to enhance learning effectiveness.[ 25 ] Scholars can also take cognizance of the capacity of AI to analyze large volumes of data, pick out patterns, and speed up scientific breakthroughs.[ 26 ] However, even with that optimism, there is evidence that optimistic feelings do not automatically lead to higher usage of AI-based learning tools. Perhaps the reason lies in the absence of institutional support and technical support. Teachers can be optimistic about AI but lack means, training, or incentives to implement AI instruction. Behavioral patterns in AI use The largest gap identified in this research was in AI utilization behavior. 6.1% of the students and 14.3% of the teachers reported high usage of AI, whereas 46.9% of the teachers and 29.8% of the students reported low usage [ Table 2 ]. The correlation [ Table 3 } and the regression results [ Table 4 ] reveal a critical nuance in AI adoption dynamics: while attitudes significantly predicted usage behavior ( β = 0.41, P = 0.01), knowledge showed no meaningful association ( β = 0.12, P = 0.32), challenging conventional technology adoption frameworks that posit awareness as a primary driver. This dissonance mirrors findings from nursing education study, where positive attitudes failed to translate to practice without institutional support,[ 27 ] yet contrasts with Indian studies demonstrating strong knowledge-behavior linkages when paired with infrastructure.[ 28 ] Specifically, our data show faculty exhibited 187% greater odds of high AI use per unit attitude increase (OR = 2.87), but only 14% achieved frequent usage despite 71% endorsement—a gap suggesting structural barriers like absent curricular mandates or unreliable digital tools mediate implementation. These patterns align with previous pre-reform stagnation,[ 29 ] where didactic AI knowledge without applied training yielded minimal behavioral change. For Indonesia, this implies that interventions must simultaneously cultivate positive perceptions through clinical AI demonstrations while addressing systemic hurdles like internet access disparities and ethical guideline ambiguities that currently decouple intention from action. Although awareness and positive attitudes toward AI are evident, actual utilization remains low, indicating a disconnect between knowledge, attitude, and practical application. Several factors may contribute to this gap, with one of the primary barriers being the lack of institutional policies and integration of AI into the medical curriculum. This institutional shortfall limits opportunities for hands-on experience with AI-based tools, thereby hindering meaningful adoption and implementation.[ 30 ] Although AI has become a significant aspect in international medical education, the majority of Indonesian medical schools have not incorporated formal AI learning modules. Thus, students as well as teaching staff depend on autonomous learning, which can cause disparities in AI proficiency. Logistical challenges also contribute to the intricacy of AI implementation.[ 31 ] Most medical schools and hospitals are poorly connected to AI-based learning materials, and they lack strong technical support. Individual faculty members are interested in using AI-based clinical decision-support tools, virtual patient simulations, or diagnostic tools but cannot because there are no institutional investments in infrastructure and training. Lastly, the absence of AI integration into competence testing and licensure exams reduces AI learning to a lower value for students and instructors.[ 24 ] In contrast to anatomy, pathology, or pharmacology, AI is not a mandatory medical licensure requirement, deterring students from willing learning with AI resources. While AI knowledge remains an unmeasured skill, the majority of students will prioritize it as an elective instead of a necessary skill for their upcoming career. It was originally assumed that more knowledge in AI would be linked with more use of AI. One of the hypotheses technology adoption models, including the Technology Acceptance Model (TAM), have used for a long time is that users with more knowledge about a system will use it more. This study, however, refutes this assumption and no correlation at all between knowledge and action among teachers ( P = 0.582) and students ( P = 0.593) [ Table 3 ]. This indicates that knowledge of AI alone does not influence adoption directly and that stronger barriers dominate the decision to use AI. The disconnect between AI knowledge and adoption suggests that psychological, institutional, and practical barriers play a more significant role in technology implementation than understanding. These barriers might include fear of job displacement, concerns about technological complexity, lack of institutional support, or deeply ingrained traditional teaching methodologies. The findings challenge the linear progression of technological adoption, demonstrating that mere awareness or comprehension of AI technologies does not automatically translate into willingness or ability to integrate them into professional practices.[ 32 ] Furthermore, the research highlights the need for a more nuanced approach to AI technology diffusion in educational settings. Simply providing training or increasing knowledge may not be sufficient to drive adoption. Instead, a comprehensive strategy addressing systemic barriers, alleviating psychological concerns, and creating supportive implementation frameworks becomes crucial.[ 33 ] This requires a holistic approach that considers not just technical training, but also change management, addressing potential resistance, providing clear value propositions, and developing robust support systems that can help educators and students overcome their hesitations about AI integration. Limitations and recommendation While this study offers valuable insights into the adoption of AI in Indonesian medical education, several limitations should be acknowledged. The cross-sectional design limits the ability to draw causal conclusions, and reliance on self-reported data may introduce social desirability bias, particularly concerning the frequency of AI use. Although the sample included participants from 17 institutions across various regions, it may not fully capture the diversity of all Indonesian medical schools—particularly those with limited access to technological infrastructure. Furthermore, the study did not differentiate between specific types of AI tools (e.g., ChatGPT versus clinical decision-support systems), which may exhibit distinct patterns of adoption and use. Conclusion This study demonstrates that while Indonesian medical students and educators recognize the potential of AI in medical education, significant gaps persist between awareness and practical adoption. These findings underscore the urgent need for policy interventions to align Indonesia’s medical education system with international digital health standards and evolving healthcare demands. Drawing on WHO’s digital health competency frameworks and Indonesia’s SKDI reform process, we recommend integrating AI literacy into core medical curricula, establishing national faculty development programs for AI integration, and investing in the technological infrastructure necessary to support these initiatives. Such measures would position Indonesia’s medical education system to produce graduates equipped with the digital competencies required for modern healthcare practice. Without these strategic investments, Indonesia risks widening the gap with global leaders in AI-enhanced medical education and healthcare delivery. Future efforts should focus on implementing and evaluating these policy changes to ensure they effectively translate into improved educational outcomes and clinical practice. Suggestions for further research Future studies should employ longitudinal designs to track AI adoption trends, incorporate objective usage metrics and explore the impact of targeted on behavioral change. Comparative studies across LMICs could identify contextual barriers and facilitators to AI integration in medical education. Abbreviations AI: Artificial Intelligence. SKDI: Standar Kompetensi Dokter Indonesia (Indonesian Doctors’ Standard Competency). Conflict of interests There are no conflicts of interest. AI policy statement During the preparation of this work, the authors used [Grammarly/GPT-4] to improve readability and sentence flow. The AI tool assisted solely with language editing, and all substantive content, analysis, and conclusions remain the original work of the authors. The final manuscript was carefully reviewed and approved by all co-authors. 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