Exploring applications of artificial intelligence in enhancing the quality of medical education: a mixed methods research synthesis

Journal of Medical Education Development Volume 18, Issue 4 e-ISSN: 2980-7670 December 2025 www.https://edujournal.zums.ac.ir Pages 119-139 Copyright © 2025 Zanjan University of Medical Sciences. Published by Zanjan University of Medical Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/bync/4.0/). Noncommercial uses of the work are permitted, provided the original work is properly cited. Exploring applications of artificial intelligence in enhancing the quality of medical education: a mixed methods research synthesis Reza Mahdi 1 , Ahmad Keykha 2* , Rogers Kaliisa 3 , Fatemeh Darabi 4 1Department of Future Studies, Institute for Cultural and Social Studies, Ministry of Science, Research and Technology, Tehran, Iran 2Sharif Policy Research Institute, Sharif University of Technology, Tehran, Iran 3Department of Education, University of Oslo, Oslo, Norway 4Department of Educational Psychology, Islamic Azad University, Science and Research Branch, Tehran, Iran Article info Abstract Introduction Medical education is a key part of higher education because it has a direct impact on the quality of care and patient outcomes. Unlike generic areas of education, medical education necessitates training of competent health professionals that combine extensive theoretical knowledge with practical as well as clinical competencies needed to contribute meaningfully to society [1]. So, the quality of medical education has a direct effect on the efficiency of the healthcare delivery system and public health [2]. There are many challenges Article history: Received 13 May. 2025 Revised 10 Jun. 2025 Accepted 26 Oct. 2025 Published 19 Nov. 2025 Background & Objective: Academic systems are among the many spheres of human life highly influenced by artificial intelligence (AI). The idea of quality in medical education is changing as a result of AI-driven developments, creating both opportunities and difficulties. The purpose of this study is to investigate how AI might be used to improve the quality of medical education. Materials & Methods: Mixed methods research synthesis was the approach taken. Relevant studies published in Science Direct, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar between 2015 and 2025 were found using targeted search terms. Quality was assessed through the Mixed Methods Appraisal Tool (MMAT) and selection process followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The final review included 49 studies that met the criteria. A model with eight dimensions of the quality of medical education was employed to analyze the data. Results: The results were grouped into eight categories: mission and goals, organizational structure and governance, faculty members, students, teaching and learning processes, curricula, facilities, and research activities. AI was found to have a positive effect on all areas, with the most focus on faculty members (38 citations) and teaching-learning processes (36 citations). It was found that these themes were very important for making education better. By comparison, mission and objectives, and research activities received little reference (8 references each), indicating strategic and research-focused AI integration lacunae. Conclusion: AI has the most potential to change how medical education is taught by using new teaching tools, better lesson plans, and personalized learning. But the fact that research and planning dimensions don't cover everything shows how important it is to do research and make policies with clear, well-defined goals. Balanced implementation of AI in all dimensions of quality is needed to bring sustainable and comprehensive transformations in medical education. The current study offers significant implications to educators, policymakers, and researchers for guiding AI-supported education reforms in the future . Keywords: artificial intelligence (AI), medical education, educational technology, quality improvement, mixed methods synthesis *Corresponding author: Ahmad Keykha, Sharif Policy Research Institute, Sharif University of Technology, Tehran, Iran Email: [email protected] Review Article How to cite this article: Mahdi R, Keykha A, Kaliisa R, Darabi F. Exploring applications of artificial intelligence in enhancing the quality of medical education: a mixed methods research synthesis. J Med Edu Dev. 2025;18(4):119–139. http://dx.doi.org/10.61882/edcj.18.4. 119 [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 1 / 21 120AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) to providing high-quality medical education, such as constantly updating the curriculum, training faculty, coming up with new ways to teach, and coming up with strong ways to test students. These efforts are meant to make sure that graduates have not only a lot of knowledge but also the important clinical and interpersonal skills they need. Improving the quality of medical education has therefore become a priority for different stakeholders such as learners, teachers, patients, healthcare workers, regulatory agencies, and financiers [3]. To address this priority, quality in medical education has drawn significant scholarly attention as researchers seek to understand and maximize it amidst evolving health needs [4]. The quality of medical education is important beyond the academe since it is irrevocably entwined with the ability of health systems to deliver safe, effective, and equitable care. All stakeholders agree that without earnest dedication to the quality of medical education, quality improvements in healthcare delivery are not feasible [5]. High-quality medical education is characterized by well-structured educational systems, comprehensive curricula, qualified faculty, and enhanced teaching practices. Furthermore, medicine graduates need to attain a blend of soft skills, practical skills, and theory-based medical understanding [6]. However, "quality" is an aggregate and comparative term and is still challenging to define as there are multiple interpretations based on stakeholders' perceptions and standards [7]. Amidst rapidly evolving international trends shaping higher education today, quality has become increasingly critical. Universities are faced with complex issues with technological advancement accelerating, calling for adaptive interventions and revolutionary changes in their function as educators [8]. Artificial Intelligence (AI), a revolutionary technological innovation, also promises to enhance the quality of education in every discipline, including medical education. The application of AI—including intelligent tutoring systems, chatbots, adaptive learning platforms, automated grading, and learning analytics—enables individualized learning experiences, enhances teaching effectiveness, and streamlines academic administration [9-13]. In addition to its well-known applications in medical training, artificial intelligence is driving a wide range of innovations across the broader field of medicine. For instance, personalized medicine has been developed with the aim of providing drugs based on the individual characteristics of patients [14]. In addition to its well-known applications in medical training, artificial intelligence is driving a wide range of innovations across the broader field of medicine [15]. Yet another key focus area is the early diagnosis of various diseases, which results in improved prevention and reduced costs of treatment [16]. Also, advances in medical imaging have provided improved analysis of radiological data, thereby improving diagnostic efficiency [17]. In medical care, AI has proven to be an effective tool for patient monitoring and surveillance [18], while clinical decision-support systems have enhanced diagnostic and therapeutic processes [19]. Medical data management has also been streamlined through advanced algorithms, enabling the analysis of large-scale health datasets [20]. In addition, genome analysis and genetic medicine have opened new pathways for treatments based on patients’ genetic profiles [21]. Virtual medical consultations and AIdriven assistants have also played a substantial role in improving patient engagement and expanding access to healthcare services [22]. Finally, AI has contributed to optimizing therapeutic processes and improving the overall efficiency of healthcare systems [23]. Despite this potential, the integration of AI into education presents several challenges. Some of these are the danger of relying too much on AI tools, which could hurt academic standards, worries about student data privacy, and the possibility of biases being built into AI platforms through algorithms that reflect human biases [24, 25]. Therefore, AI should not be regarded as a universal remedy, but rather as a supportive instrument whose efficacy is contingent upon the establishment of suitable safeguards. Current studies underscore these dual facets of AI in education. Kabudi [26] identifies critical areas such as educator roles, support for special-needs students, racial and data bias in AI-based learning systems, and commercialization challenges. Flores-Viva and García-Peñalvo [27] emphasize ethical considerations in AI’s educational applications. Nkechi et al. [28] show how AI can help close the digital divide and overcome language barriers to make education more equal. Ajani et al. [29] talk about how AI can help with personalized learning, better teaching methods, and more efficient administration. They also talk about privacy and ethical issues. AlSagri and Sohail [30] argue that deeper understanding of AI capabilities can inform policies fostering equitable and sustainable education systems. This study employs a mixed methods research synthesis approach to address international research gaps regarding AI’s role in improving education quality, with a novel focus on medical education. The results give a [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 2 / 21 121AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) broad, worldwide view of how AI could improve the quality of medical education. This study helps close the knowledge gap by giving educators, policymakers, and researchers who want to use AI to improve the quality of medical education useful information. This study adheres to the most prevalent framework for evaluating the intrinsic quality of university teaching [31–35]. There are eight principal dimensions in the conceptual model: Mission and Objectives, Organizational Structure and Management, Faculty Members and Students, TeachingLearning Processes, Educational Programs and Curricula, Educational and Research Facilities, and Research Activities. Materials & Methods Design and setting(s) Since both quantitative and qualitative studies, as well as those employing mixed-method research designs, are available for examining the applications of artificial intelligence in quality of medical education, we chose an integrated Mixed-Methods Research Synthesis (MMRS) design as the methodological framework for our review. A mixed-methods research synthesis is a form of systematic review that applies the principles of mixedmethods inquiry. In essence, such a study is expected not only to include two distinct strands—one qualitative and one quantitative—each with its own questions, data, analyses, and conclusions—but also to integrate, link, or connect these strands in a meaningful way [36]. Specifically, we adopted the MMRS framework developed by Heyvaert and colleagues [37]. A diagrammatic overview of this framework can be found in Figure 1. Considering the multidimensional complexity of artificial intelligence applications quality of medical education, it seems essential to employ a mixed-methods approach in the synthesis of the studies (Figure 1). Quantitative studies primarily seek to investigate the effectiveness of AI tools by measuring the performance improvement of learners, or system accuracy, while qualitative studies evaluate attitudes, experiences, and ethical and educational dilemmas with the implementation of this technology. A synthesis of these two types of data provides a richer understanding of the phenomenon. On one hand, quantitative data provide empirical and objective evidence about efficiency and outcomes; on the other hand, qualitative data primarily offer enhanced insights into the contexts, meanings, and processes associated with the use of AI in quality of medical education. In a methodological sense, a mixed synthesis approach is especially relevant for publishing the synthesis or results of qualitative and quantitative studies, as it allows for interpretation of these diverse findings within a unifying framework. In this manuscript, and as shown in Figure 1, data from the base qualitative and quantitative studies were first synthesized in their separate studies. In the last stage, through an integrative synthesis, similarities, differences, and complementarity across the two strands were assessed. The framework provides a six-stage process [37], and subsequent sections of this report describe the process we undertook for each of the particular six stages of this study. Figure 1. Qualitative, quantitative, and mixed methods research synthesis [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 3 / 21 122AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) The problem identification and question formulation stage The research question was designed using the standard PICo/PICO framework, which helps researchers define the scope and objectives of their study with precision and clarity. In this study, Population (P) refers to students, faculty members, administrators, and other stakeholders in both medical and non-medical education systems, who are considered the primary users or beneficiaries of AI applications. Interest/Intervention (I) represents the phenomenon under study, namely the application of Artificial Intelligence in improving the quality of medical education. Context (C) refers to the academic settings, including higher education institutions and medical training centers where AI technologies have been integrated into educational processes. To ensure methodological rigor, the search strategy was explicitly derived from this framework. The Population (P) was operationalized into keywords such as “medical students,” “residents,” “faculty,” and “medical education stakeholders.” The Intervention (I) generated terms including “artificial intelligence,” “AI,” “machine learning,” “deep learning,” “chatbots,” and “educational technology.” The Comparison (C) dimension was not uniformly applicable across studies due to the heterogeneity of their designs. However, where relevant, comparative terms such as “traditional education,” “nonAI learning,” or “conventional teaching” were included. Finally, the Outcomes (O) were framed in terms of “quality improvement,” “learning outcomes,” “teaching effectiveness,” and “educational quality,” which were translated into keywords such as “quality of medical education,” “teaching quality,” and “educational improvement.” By systematically mapping these PICo/PICO elements into database search strings, we ensured comprehensive coverage of the literature relevant to AI applications in enhancing the quality of medical education. Grounded on such considerations, this research question was formulated: What are the applications of AI in enhancing the quality of medical education? To ensure accuracy in answering this question, key constructs were operationally defined to provide consistency and clarity in study selection and data analysis. AI applications were classified as any digital or computational systems that utilize machine learning, natural language processing, deep learning, expert systems, or other associated intelligent technologies that are applied within educational environments. These included, but were not limited to, intelligent tutoring systems, adaptive learning systems, chatbots, automated grading systems, virtual or augmented reality made easier by AI, predictive analytics, and AI-driven academic management systems. We looked at the following categories when it came to levels of medical education: Undergraduate Medical Education (UME): Basic programs that lead to the first medical degrees (like MBBS or MD); Postgraduate Medical Education (Residency and Fellowship): Structured training programs for specialists after undergraduate medical education; Continuing Medical Education (CME): Formal and informal learning activities that help practicing doctors and other healthcare professionals keep their skills up to date and improve them; Interprofessional Education (IPE): Learning spaces where medical students and allied health professionals learn together using AI-enabled tools. Only research that specifically addressed the application of AI at one or more of these levels of medical education, with a clear connection to quality improvement, was considered eligible for analysis. The development of a review protocol and the literature search The study protocol was crafted to facilitate mixed methods research synthesis MMRS framework regarding the utilization of AI in the enhancement of medical education quality. The creation of the protocol needed multiple steps: crafting the research question and study objectives; choosing a conceptual framework; setting up inclusion and exclusion criteria; devising the search strategy and resource retrieval techniques; delineating the screening and selection process for studies; choosing tools to evaluate study quality; deciding on the data analysis method; and validating and confirming the results. We did a systematic search of the literature for peer-reviewed articles that were published in English between 2015 and 2025. This decade was chosen to include the era of large progress in machine learning, deep learning, and natural language processing, which have become essential in revolutionizing educational assessment and improving quality. The electronic databases Science Direct, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar were all searched. With an emphasis on (O) Quality Improvement, the search strategy combined keywords associated with (I) Artificial Intelligence and (P) Medical Education in a Boolean query based on the PICO framework. For every database, the search string [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 4 / 21 123AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) was modified. Below is a representative example: An example of a search query is: ("artificial intelligence" OR AI OR "machine learning" OR chatbot*). AND ("clinical training" OR "medical student*" OR "medical education") AND ("learning outcome*" OR "educational quality" OR "quality improvement") The following standards served as a guide for choosing the study: Criteria for inclusion: publications from 2015– 2025 that are peer-reviewed in English, specifically address AI applications in medical education, and use either mixed-methods, quantitative, or qualitative research. Exclusion criteria include: studies done outside of the designated time frame; editorials, opinion pieces, and non-peer-reviewed literature; and articles that do not focus on improving educational quality. Look at the selection procedure: As shown in Figure 2, the PRISMA guidelines were followed during the study selection process. 187 records were found in the first database search. 77 studies were excluded after the titles and abstracts of these records were screened and duplicates were removed. After abstract review, 43 of the 110 remaining studies were eliminated. Following a fulltext evaluation of the 67 studies that were found, 18 articles that did not fit the inclusion criteria were eliminated. The MMAT (Mixed Methods Appraisal Tool, 2018 version) was adopted to assess the quality of the methodological approach of all the studies included in this synthesis. Each study was reviewed based on five criteria followed by a score of between zero to five. The criteria cut-off was defined to be any study that scored three or above, which is demonstrative of a compromise between quality and feasibility. Lower scores indicate methodological concerns with confidence and an increased risk of bias which may affect confidence in the findings from this synthesis. As such a score of three or above is the minimum standard of quality and validity to avoid studies that demonstrate methodological concerns from being included in the synthesis. The MMAT scores for studies included in this synthesis are shown in Table 1. Figure 2. PRISMA flowchart for screening the articles [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 5 / 21 124AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) Table 1. Bibliography of the selected articles Reference Authors Year Article title Country Study designs MMAT 38 Merliana & Tantri 2022 Improving the quality of Hindu education in the era of Society 5.0 through digital culture Indonesia Literature review with descriptive qualitative approach 3.5 39 Somasundaram et al. 2020 Artificial intelligence (AI) enabled intelligent quality management system (IQMS) for personalized learning path India Conceptualdescriptive 3.5 40 Ibrahim 2024 The effects of implementing artificial intelligence systems on enhancing educational services' quality from the perspective of employees at Alzaiem Alazhari UniversitySudan Sudan Descriptiveanalytical 4 41 Sahari 2024 Exploring the role of AI in advancing quality education in higher institutions for sustainable development India Qualitative 4 42 Muminov 2024 Development of artificial intelligence and its impact on educational quality in public schools Uzbekistan Mixed-methods 3.5 43 Arruda & Arruda 2024 Artificial intelligence for SDG 4 of the 2030 agenda: transforming education to achieve quality, equality, and inclusion Brazil Qualitative 3.5 44 Buaton et al. 2022 Optimization of higher education internal quality audits based on artificial intelligence Indonesia Applieddevelopmental 3.5 45 Judijanto et al. 2023 Student sentiment analysis: implementation of artificial intelligence in improving teaching quality Indonesia Quantitative 3.5 46 Sayfullayeva 2024 The role of artificial intelligence in improving the quality of student learning process Indonesia Qualitative 3.5 47 Abdullayev 2023 Harnessing technologies for enhancing the quality of higher education Uzbekistan Literature review 4 48 Nugroho & Anwar 2023 Implementation of artificial intelligence in increasing the quality of learning Indonesia Qualitative 3.5 49 Shikokoti & Mutegi 2024 Influence of artificial intelligence on the quality of education in higher learning: a case study of Faculty of Education, University of Nairobi, Kenya Kenya Mixed-methods 4.5 50 Lei 2017 Modern educational technology theory and university quality education China Literature review 3 51 Chemlal & Azouazi 2023 Implementing quality assurance practices in teaching machine learning in higher education Morocco Literature review 3 52 Wang 2023 The high-quality development path of education from the perspective of digitization China Literature review 3 53 Chekirine & Zoubida 2024 Artificial intelligence's impact on higher education quality Algeria Literature review 3 54 Hafiiak et al. 2019 Information technology as a component of improving the training quality future specialists in higher education institutions Ukraine Literature review 3 55 Peñalvo et al. 2024 Safe, transparent, and ethical artificial intelligence: keys to quality sustainable education (SDG4) Spain Qualitative 4.5 56 Li & Su 2020 Evaluation of online teaching quality of basic education based on artificial intelligence China Mixed-methods 3 57 Yang 2022 Digital transformation to advance the high-quality development of higher education China Qualitative 3.5 58 Hussain et al. 2022 The effect of the artificial intelligence on learning quality & practices in higher education India Qualitative 3.5 59 Flores-Viva & GarcíaPeñalvo 2023 Reflections on the ethics, potential, and challenges of artificial intelligence in the framework of quality education (SDG4) Spain Qualitative 4.5 60 Perminova et al. 2023 The role of artificial intelligence in improving the quality of education and research Ukraine Qualitative 4 61 Yuan et al. 2020 Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19 China Quantitative 3 62 Patil 2024 The potential of AI in enhancing education access and quality India Qualitative 4 63 Ajani et al. 2024 Leveraging artificial intelligence to enhance teaching and learning in higher education: promoting quality education and critical engagement South Africa Literature review 4.5 64 Lv 2021 Research on evaluation of teaching quality of Marxist theory in massive open online course based on artificial intelligence China Quantitative 4 65 Yugandhar & Rao 2024 Artificial intelligence in classroom management: improving instructional quality of English class with AI tools India Qualitative 3.5 66 Li & Wang 2023 Artificial intelligence and edge computing for teaching quality evaluation based on 5G-enabled wireless communication technology China Quantitative 4 67 Altinay et al. 2024 Capacity building for student teachers in learning, teaching artificial intelligence for quality of education Turkey Qualitative 4 68 Rane 2023 Enhancing the quality of teaching and learning through ChatGPT and similar large language models: challenges, future prospects, and ethical considerations in education India Qualitative 3.5 69 Utkirov 2024 Artificial intelligence impact on higher education quality and efficiency Uzbekistan Mixed-methods 4 70 Verma et al. 2021 Study of AI techniques in quality educations: challenges and recent progress India Literature review 4 71 RodríguezAbitia et al. 2020 Digital gap in universities and challenges for quality education: a diagnostic study in Mexico and Spain Mexico and Spain Qualitative 3 72 Nedungadi et al. 2024 The transformative power of generative artificial intelligence for achieving the sustainable development goal of quality education India and Taiwan Literature review 4 73 Rahayu 2023 Analyzing of using educational technology to improve the quality and equity of learning outcomes at Politeknik Maritim Negeri Indonesia Qualitative 3.5 74 Ahmed 2024 The role of artificial intelligence applications in enhancing the quality of online higher education Saudi Arabia Qualitative 4 75 Ahmad et al. 2024 Transforming teaching learning with chatbots in higher education: quest, opportunities and challenges for quality enhancement Pakistan Qualitative 4 76 Nie 2023 Research on improving education quality and efficiency through artificial intelligence and big data analysis China Mixed-methods 3 [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 6 / 21 125AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) The selection of an appropriate design and method The third phase of this investigation selected the mixed content analysis approach as the primary analytical lens in order to determine the appropriate synthesis design and method. The goal of the research is to achieve an in-depth understanding of the uses of artificial intelligence in medical education through the integration of quantitative and qualitative evidence, and as such, content analysis offers a mechanism by which to provide shared meaning units to represent the diverse findings of primary studies, and identify categories and themes from the units. Further, within this methodological approach, data from quantitative studies have also been transformed into classizable units of meaning via content-based extraction and description—for example, by identifying indicators, variables, statistical findings or evidence statements— that can also be analyzed alongside qualitative findings. This methodological approach design has constituted a synthesis that is grounded in meaning and content as opposed to data type, thus generating a coherent and multi-faceted account of applications of artificial intelligence in the quality of medical education . The data extraction and evaluation stage The articles' texts were read several times to help readers become fully immersed in the content before the data was extracted. Key sections of the articles were documented in a structured format using Microsoft Word. For identifying key concepts—those conveying the most significant meanings—the paragraph was utilized as the unit of analysis, from which the most critical concepts were extracted. Throughout the analysis process, memos were employed extensively. During the initial reading of the texts, researchers made notes on key points, trends, or potential connections. The notes included preliminary impressions of the focal issues of the articles, key ideas related to the research issue, and emerging ideas or issues worth investigating. During coding, memos aided the recording of the rationale for why the codes were chosen, meaningmaking, and interconnection, thus promoting conversation among the researchers. In later data organization and extraction, memos proved helpful to aid the research team to compile suitable codes to create categories and overall themes. Examples of how to extract codes are shown in Table 2. Table 2. Examples of how to extract codes Extracted code sample Example sentence Curriculum personalization "In curriculum design and planning, each student’s preferences for the career path and his/her aptitude will be tested; based on the aptitude, his/her curriculum will be designed" [39, p. 441]. Personalize learning, personalized feedback, track student academic performance "AI can personalize learning. AIpowered systems can track student progress and provide individualized feedback, helping students learn at their own pace and tailored to their individual needs" [41, p. 2]. The data analysis and interpretation stage We followed Elo and Kyngas [77] This framework for examining information is built upon three foundational pillars. The initial stage focuses on establishing a foundation, where information is gathered and a structure for categorization is defined. The subsequent stage, concerned with systematic arrangement, involves a meticulous review and sorting of the data based on its alignment with pre-established classifications. The final and culminating stage is dedicated to presenting the outcomes, which articulates the findings to enable a deeper understanding of the identified patterns. Furthermore, the insights derived from this process can be significantly augmented by integrating a mixedmethods approach that incorporates both statistical and interpretive techniques. To ensure consistency, the concepts derived from each classification session were compared to measure the degree of agreement between the two coding processes. The process involved labeling similar codes across the two-time intervals as "agreement" and dissimilar codes as "disagreement." The frequency of agreements and disagreements was then used in a formula to calculate the reliability of the coding process over time. This study conducted coding and classification twice, with a 14-day interval between sessions. A total of 403 concepts were extracted, with agreement reached on 175 of them between the two coding sessions. The calculated reliability for this retest [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 7 / 21 126AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) was 86%, which, since it exceeds the 60% threshold, confirms the reliability of the coding process [78]. The reporting and discussing of research findings Finally, the findings were systematically documented and shared, ensuring a comprehensive and rigorous analysis process. Results As stated earlier, we used a standardized framework for quality assessment, which has been widely accepted in many studies, as a standard by which to measure the caliber of medical education. To ensure alignment with this framework, key concepts were extracted and then categorized according to their similarities and differences. Mission and objectives, organizational structure and management, faculty, students, teaching-learning processes, educational programs and curricula, academic and research facilities, and research activities are the eight primary dimensions that make up the framework. Within this framework, the integrated key concepts were taken into consideration as criteria. The departmental goals and missions are covered by the Mission and Objectives dimension. Academic departments' organizational setup, including policies, staffing levels, and services, is covered by the Organizational Structure and Management dimension. Academic counseling, consultation accessibility, and faculty-student interactions are all included in the Faculty Members dimension. The Students dimension covers student activities like participation in departmental planning, awareness of departmental goals, and knowledge of their rights and responsibilities. Additionally, teaching and learning strategies, technology integration, and student assessment procedures are all included in the Instruction-Learning Processes dimension. The academic programs themselves, including their alignment with departmental missions and their design in accordance with curriculum planning principles, are covered by the Educational Programs and Curricula dimension. The infrastructure and resources that are available, including libraries, information systems, and technological services, are covered by the Educational and Research Facilities dimension. Lastly, academic work done by both students and faculty is included in the Research Activities dimension. The analysis's specific results are shown in Tables 3 and 4. The frequency of references to different aspects of quality assessment in medical education, as indicated by the data in the table, stresses the significance and precedence of each aspect in related research. Teaching-learning processes (36 references) and faculty members (38 references) are given the highest priority, highlighting the critical roles that creative learning strategies, teaching approaches, and faculty engagement play in determining the quality of education. These results highlight the value of AI tools and cuttingedge technologies in improving the teaching and learning processes. 32 of the included studies made reference to the students dimension, and 34 references to organizational structure and management show that they actively take part in creating and carrying out successful educational initiatives. The development of data-driven strategies and effective resource management targeted at ongoing quality improvement in medical education are supported by highlighting these dimensions. However, dimensions like Mission and Objectives and Research Activities—of which there are only eight— seem to be less closely looked at in the studies. The intricacy of assessing these aspects or a stronger emphasis on more realistic and concrete aspects could be the cause of this. Additionally, educational programs and curricula (21 times) and educational and research facilities (14 times) show how infrastructure and curriculum design quality directly affect the quality of the learning experience. In order to guarantee a more uniform improvement in the quality of education, this frequency distribution pattern emphasizes the necessity of giving different aspects equal attention, with a special emphasis on the development of infrastructure and research initiatives. The frequency of each dimension is shown in the bar chart that follows (Figure 3). In Table 4, examples of AI applications in enhancing the quality of medical education are presented, categorized according to each dimension of the framework. [ DOI: 10.61882/edcj.18.4.119 ] [ Downloaded from edujournal.zums.ac.ir on 2025-12-17 ] 8 / 21 127AI AND MEDICAL EDUCATION J Med Edu Dev 2025;18(4) Table 3: Analysis of findings based on the standard framework for assessing the quality of medical education Dimensions Criteria Sample references Mission and objectives The development of policies in goal setting, plays a crucial role in shaping educational missions and frameworks for strategic goal-setting, assists in determining the necessary performance criteria to achieve quality objectives, while analyzing the relationship between educational goals and the execution of performance evaluations, additionally, it supports the identification of new goals, helps track outcomes, and ensures alignment with educational objectives, furthermore, it clarifies goals and values, aids in aligning objectives, and supports digital strategic planning for defining missions. [42, 45, 49, 51, 53, 55, 57, 69] Organizational structure and management Data-driven decision making; culture of innovation; transformation in design and strategy in organizational architecture; transformation in policy-making approaches; timely delivery of educational services; responsible response to complaints and challenges; committed leadership; digital leadership; development of quality culture with a digital approach; development of management information systems and dashboards; increasing transparency in administrative systems; digitalization of administrative processes; sentiment and emotion analysis of educational service recipients; prediction and provision of solutions for administrative system issues; enhancing organizational employee performance; increasing organizational commitment to quality; optimization of financial resource management; reducing administrative costs; time management in administrative processes; assisting in the delivery of standardized educational services; helping university managers in providing efficient strategies; supporting informed decision-making based on performance; reviewing existing regulations; formulating laws on the use of AI; optimizing monitoring mechanisms for educational processes; organizing, integrating, registering, and developing intelligent systems for data mining; assisting in the formulation of management strategies; supporting optimal allocation of financial resources; weighting and optimizing internal evaluation standards; comparing standards in internal evaluation; developing Blockchain approaches in certification issuance; streamlining university management systems; identifying organizational needs; developing program evaluation mechanisms; identifying gaps in internal evaluation processes; developing financial mechanisms; reorganizing and optimally allocating financial resources; reducing managers' time in problem-solving; eliminating manual administrative tasks and improving administrative processes; optimizing and automating information processes; developing information modeling methods; information-based management; developing an information culture; adapting to various organizational needs; IT-based university governance; more flexible organizational structure; organizational information management; reducing organizational levels and hierarchies; expanding the scope of management; financial resource management; intelligent human resource management; helping design applicable and practical evaluation indicators; facilitating data collection for evaluation processes; quality management; developing reporting and feedback systems in evaluation. [38, 40–44, 47, 49, 50, 53–57, 59, 61–63, 66, 69–71, 76] Faculty members Helping predict student performance accurately; helping identify strengths, weaknesses, and knowledge gaps of students; receiving personalized advice for faculty members; designing lesson plans; designing timetables for each course; ability to infer new knowledge from existing knowledge; assisting in aligning teaching strategies with students' learning styles, abilities, and needs; supporting and enhancing teaching methods; automating tasks and reducing workload; assisting in comparative evaluation; helping provide innovative teaching methods; helping identify areas where students at risk need further attention and improvement; analyzing factors and identifying patterns in student data; providing timely interventions for students in need of support; developing analytical and evaluation skills; providing student-centered education; evaluating student progress over time; helping with professional development; automated grading systems in assessments; improving evaluation accuracy; helping in continuous knowledge updating; analyzing data from various sources for evaluation; analyzing attendance and behavioral data; smart pedagogy; educational robots as assistants; helping evaluate teaching effectiveness; assisting in self-teaching planning; automatic assessment; innovation in teaching methods; assisting in designing collaborative projects; innovative teaching methods; helping set materials and difficulty levels based on individual student abilities; predictive analysis to identify complex learning behavior patterns; identifying potential future learning problems; supporting educational activities; assisting in real-time monitoring of learning progress; helping design assignments; helping design exam questions; automatic grading of exams; boosting confidence; real-time evaluation; helping provide insights into student learning patterns; customized evaluations; providing critical insights for revising teaching methods; generating new educational ideas; online assessments; theoretical innovation in education; adopting modern teaching practices; helping identify learning needs; designing learning activities; improving and optimizing teaching design; new teaching forms; developing exploratory teaching methods; collaborative teaching methods; sharing educational resources; assisting in time management; computer-based education; personalized teaching methods; helping provide relevant explanations and examples; identifying the shortest paths to deliver course content; identifying innovations in education; adjusting educational tasks; assisting in lectures; innovation in evaluation; data-driven evaluation; diversifying evaluation methods; teaching supported by big data; evaluating students' online behaviors; enriching educational resources; web-based education; helping assess student competencies; designing extracurricular activities; helping [39–50, 52–68, 72–76] Students Developing career paths for each student; AI tools as student mentors; receiving personalized advice for students; receiving real-time feedback; creating a deeper understanding of course content; encouraging students to engage with educational material; helping increase participation; boosting motivation for learning; assisting in better understanding of course content; receiving smart feedback; supporting self-development; chatbots as private tutors; voice assistants; helping students with special needs; increasing problem-solving abilities; improving virtual interactions among students; improving academic outcomes; increasing academic satisfaction; helping cultivate creativity; assisting with assignments; helping develop soft and hard skills; acting as an academic guide; helping with time management; simulating exam questions; self-learning; assisting with translation into different languages; developing personalized learning paths; helping acquire new skills; assisting with adapting to the changing job market needs; reducing cognitive load; helping absorb and digest key points and complex materials; assisting with academic planning; helping with team building and teamwork; developing digital competencies; preparing students for future industry trends; helping students achieve learning goals; simplifying assessment processes; providing additional practice opportunities and resources; strengthening self-efficacy; meaningful learning; familiarizing with new study methods; increasing adaptability and empathy skills; enhancing writing skills; boosting employability skills. 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