Introduction

Assessment is an essential element of the educational process and provides evidence of how students achieve learning objectives and maintain teaching standards. Assessment is the cornerstone of the educational process, providing evidence of student learning and informing teaching practices ( 1 , 2 ). Medical education assessment is primarily based on ranking students in terms of their knowledge and attitudes, as well as evaluating their clinical competence ( 3 , 4 ). However, the increasing complexity of medical science necessitates innovative approaches that can enhance both the efficiency and effectiveness of assessment. Intelligent Assessment Systems (IAS) represent a promising solution. By leveraging artificial intelligence and advanced technologies, IAS can offer personalized and efficient assessment, providing timely and actionable feedback to both students and instructors ( 5 - 7 ). This can lead to improved learning outcomes, enhanced student engagement, and more effective teaching strategies ( 6 , 8 , 9 ). Intelligent assessment systems refer to technologies and systems that utilize artificial intelligence and advanced technologies to assess the learning progress of learners. Intelligent assessment systems offer numerous benefits in education for both students and instructors, including precise and timely feedback, fair evaluation, improved learning outcomes, personalized instruction and assessment, and the use of advanced and comprehensive assessment methods ( 10 , 11 ).

In summary, Intelligent Assessment Systems (IAS) are the systems that, through artificial intelligence and advanced algorithms, are capable of accurately and rapidly assessing the performance and learning of learners in various domains. Figure 1 illustrates the benefits and applications of Intelligent Assessment Systems (IAS) in medical education. These systems leverage advanced technologies to enhance the educational process by identifying the students' strengths and weaknesses, providing precise and timely feedback, and enabling fair evaluation. IAS also facilitates advanced assessment methods, personalized instruction, and improved learning outcomes. Moreover, they reduce the time and cost of training, enhance medical curricula, and promote group and virtual collaboration. By focusing on content creation and research, IAS supports educators and students in achieving higher educational standards and outcomes. This visual summary highlights the multidimensional advantages of IAS and their potential impact on transforming medical education (Figure 1) ( 12 - 14 ).

Figure 1. Mind Map of Key Concepts in Intelligent Assessment Systems (Authors' own work), Illustration of various benefits and applications of Intelligent Assessment Systems (IAS) in medical education

IAS can reduce the time and cost of training and focus on content creation and research problem-solving. Furthermore, by considering the feedback and suggestions of an IAS, educators can improve their medical curriculum and achieve greater success in conveying the concepts, methods, and relevant perspectives by identifying teaching shortcomings and challenges in this field ( 15 - 17 ).

With the use of IAS, students can independently and automatically tackle various challenges in the field of medical sciences and compare their results with scientific standards. Additionally, by receiving regular and transparent feedback from IAS, students can identify their strengths and weaknesses and work on improving their performance ( 18 - 20 ).

Students can work with their peers in group and virtual environments and benefit from their experiences and knowledge. Moreover, by utilizing the communication and collaborative tools provided by IAS, students can share their opinions, suggestions, and criticisms with a wider range of professors and fellow students and learn from them ( 21 - 24 ).

Currently, many medical universities around the world are utilizing intelligent assessment systems. However, the advantages and limitations of these systems for medical sciences have not been extensively studied and are less known. One major issue is the risk of bias embedded in AI algorithms, which can lead to unfair evaluations, particularly for diverse learner populations. Data security and privacy are also significant concerns as these systems often require sensitive personal information to function effectively. Moreover, the integration of IAS into existing curricula can be complex, requiring substantial time, training, and infrastructure ( 25 , 26 ).

Despite the growing interest in IAS, there is a need for a comprehensive understanding of their impact on medical education ( 27 , 28 ). While many medical schools have adopted IAS, the evidence for their effectiveness remains limited. This systematic review aims to address this gap by systematically evaluating the existing literature on using IAS in medical education. By examining the advantages and limitations of IAS, this study provides valuable insights for educators, researchers, and policymakers. This knowledge can inform the development and implementation of effective IAS, ultimately contributing to the advancement of medical education and the training of highly skilled healthcare professionals.

Moreover, this research can familiarize educators, medical researchers, and students with the advantages and limitations of Intelligent Assessment Systems (IAS), enabling them to utilize these technologies to achieve their educational goals ( 22 , 29 , 30 ). This research can assist university administrators and policymakers in making better decisions regarding the selection and implementation of Intelligent Assessment Systems (IAS). This study investigates whether Intelligent Assessment Systems (IAS) improve educational processes, academic evaluation, and student progress compared to traditional methods. Moreover, this systematic review highlights the role of Intelligent Assessment Systems (IAS) in improving student outcomes, delivering instant feedback, and reducing the workload of instructors.

Methods

This research is a systematic review study conducted using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) framework for reporting systematic review articles. The research was approved by the ethics committee of AUMS (IR.ABADANUMS.REC.1402.092).

Search Strategy

A detailed search was conducted in Embase, PubMed, ProQuest, Web of Science, and Scopus from January 2010 to March 2024 (articles published until March 2024). Keywords and MESH terms were utilized.

Screening Process

Two researchers independently reviewed and resolved disagreements through consensus.

Data Extraction

Information on the outcomes, methodologies, and limitations was synthesized into a structured data table. A 7-step approach began by formulating the research question, followed by identifying relevant studies, selecting appropriate studies, and synthesizing and reporting the data. In the first stage, the PIO method was utilized to clearly state the research question in this study. Accordingly, the study population consisted of medical science students and instructors, and the intervention under investigation was the use of intelligent assessment systems in educational and evaluative processes, and the outcome assessed was the improvement in the performance of students and instructors in educational and evaluative processes. The research question was posed as follows: "Does the use of intelligent assessment systems (IAS) lead to improvements in educational processes, academic evaluation, and learning progress of medical science students, compared to traditional assessment methods?"

In the second stage of the study protocol, a brief review of existing studies was conducted to find relevant literature. MESH terms and keywords were used to identify the best keywords for the search. Using keywords such as Intelligent Assessment Systems, Educational Processes, Educational Assessment, Medical Sciences Achievements, Challenges, and their equivalents, we developed a search strategy (Table 1). Based on the inclusion criteria, the desired articles and theses were searched in databases including Embase, PubMed, ProQuest, Web of sciences, and Scopus until March 2024 by two researchers.

In the third stage, inclusion and exclusion criteria for the articles in this study were developed. The inclusion criteria consisted of having the search keywords in the Title, Abstract, or Keywords of the articles; being relevant to the study objective and research question; being written in the English language, and being peer-reviewed. The exclusion criteria included articles unrelated to the study objective, secondary studies such as review articles and conference papers, and articles published in non-academic journals or incomplete articles.

In the fourth stage, duplicate articles were removed from the study using the reference management software EndNote 21.2. Then, the titles of all articles from the databases were reviewed by two researchers. Articles relevant to the research question and with the inclusion criteria were selected. In the next stage, the abstracts of the selected articles were read by two researchers. Subsequently, articles that were completely aligned with the study objective and met the inclusion criteria were selected. The full text of these articles was read and evaluated by two authors. Finally, articles related to the role of Intelligent Assessment Systems (IAS) in improving educational processes and assessment in medical sciences were selected (Figure 2).

Figure 2. Identification and selection of studies through databases

In the fifth stage, the methodological rigor of the selected studies was evaluated using established criteria for assessing research quality, data validity and reliability, generalizability, and the quality of journals based on impact factors. Valid and approved articles were selected for inclusion in the study (Table 2).

In the sixth stage, key concepts relevant to the study objectives were extracted, summarized, and categorized from the selected articles in a data extraction table. Common elements and themes in the articles were identified based on the specific research question (Table 3).

The seventh stage involved the analysis of the results and discussion regarding the study title and addressing the predetermined research question based on the previous stages.

Articles selection

The search of electronic databases extracted 2,741 articles, which were then imported into the EndNote software for management. After removing 856 duplicate articles, 1,885 studies remained based on their titles and abstracts. Two separate researchers reviewed these studies to select articles relevant for full-text examination. Discrepancies in article selection were identified, deliberated upon, and ultimately resolved through consensus among the research team.

Of these remaining articles, 455 studies were removed after reviewing the title; the remaining 93 articles were studied based on the abstract by two researchers; due to incompatibility with the research topic, lack of connection with the research question, and lack of compliance with research standards, 50 articles were removed. 43 articles entered the full-text review phase, and after reviewing and reading the articles fully, 10 articles were selected to participate in this systematic review (Table 4).

Results

A thorough analysis was conducted on the eleven final articles selected for this systematic review, all of which focused on the role of Intelligent Assessment Systems (IAS) in medical education. These studies varied in design, sample size, and data collection methods, providing a comprehensive overview of the field.

The selected studies included both quantitative and qualitative research, with sample sizes ranging from small pilot studies involving fewer than 30 participants to large-scale studies encompassing over 500 participants. Data collection methods varied and included surveys, interviews, observational methods, and performance data derived from the implementation of Intelligent Assessment Systems (IAS). Most studies exhibited high reliability and validity although a few were limited by sample size and generalizability (Table 2). This thorough evaluation ensures that the findings are robust and credible, notwithstanding these few limitations.

Key findings from the review indicate significant enhancements in educational processes through the use of Intelligent Assessment Systems (IAS). A study by Wei Wu (2021) ( 31 ) explored the effectiveness of an Intelligent Assessment System (IAS) in improving teaching and learning. The study showed a 40% decrease in grading time, with the most striking improvement being the reduction in feedback delivery time - from the traditional 24-48 hour window to just 2-3 minutes through automated assessment, resulting in measurable improvements in student performance. The findings revealed a two-fold benefit. Firstly, IAS significantly reduced the time educators spent on grading and administrative tasks, leading to improved teaching efficiency. This freed up valuable time for educators to focus on core activities like instruction and research. Secondly, the study found that using IAS to assess student performance enhanced the students’ learning outcomes. However, the implementation of IAS also presented challenges. The system required significant technical support and maintenance. Additionally, some students still necessitated supplemental support and guidance from teachers.

The study by Grivokostopoulou and colleagues (2017) showed the effectiveness of their automated assessment system in higher education. Their findings highlighted three key achievements: the successful implementation of systematic error categorization in student responses, an impressive 92% accuracy rate in automated marking, and enhanced student performance attributed to the immediate feedback mechanisms. These results demonstrated the potential of automated assessment systems to both improve evaluation accuracy and support student learning through timely feedback ( 32 ).

A literature review was conducted by Alexander Winkler-Schwartz, which investigated machine learning algorithms to assess surgical expertise and virtual reality simulators to evaluate technical skills. The study aimed to improve the assessment of surgical expertise using machine learning algorithms and enhance the understanding of psychomotor performance in virtual reality simulations. The anticipated outcomes included increased accuracy in assessing surgical expertise, better discrimination between expert and novice performers, and improved training and education in surgical procedures. The research also aimed to develop a standardized framework for reporting virtual reality studies using machine learning algorithms and to identify key elements for assessing surgical expertise in virtual reality simulations. The review acknowledged limited interdisciplinary communication and knowledge transfer between computer science, medicine, and education, as well as important differences in reporting between medical and computer science journals. To address these challenges, the authors suggest that the Machine Learning to Assess Surgical Expertise (MLASE) checklist can help ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. The checklist complements existing guidelines for best practices in reporting experimental design in medical education. The authors emphasize the need for further research to fully explore the potential of machine learning in surgical education ( 33 ).

Intelligent Assessment Systems (IAS) are revolutionizing medical education by providing innovative tools for evaluating student performance and enhancing learning outcomes. Machine learning algorithms, a key component of IAS, have demonstrated remarkable potential in assessing surgical expertise within virtual reality (VR) simulations. This innovative approach offers a safe and controlled environment to evaluate a surgeon's technical skills, enabling more accurate differentiation between expert and novice performers. As highlighted by Alexander Winkler-Schwartz in 2019, the use of IAS can significantly improve surgical training and education.

Fazlollahi investigated the effect of artificial intelligence tutoring compared to expert instruction on medical students’ learning simulated surgical skills. The study population consisted of 70 medical students who participated in a simulated operation. The interventions included five feedback sessions, each lasting five minutes, during a single 75-minute training session encompassing five practice sessions followed by one realistic virtual reality brain tumor resection. The results indicated that virtual operative assistant (VOA) significantly improved practice Expertise Scores by 0.66 (95% CI, 0.55 to 0.77) points compared with the instructor group and by 0.65 (95% CI, 0.54 to 0.77) points compared with the control group. The study found that VOA feedback improved the technical performance and learning outcomes of medical students during brain tumor resection simulations. The research demonstrated the potential of AI tutoring systems to enhance surgical skill acquisition and transfer and to provide objective quantitative assessment of technical skills. Acknowledging the limited availability of expert instructors, the study suggests that VOA feedback is a valuable tool for improving surgical skills training and may provide a solution to the challenges posed by the COVID-19 pandemic. However, the authors note the need for further validation of the AI tutoring system in larger samples ( 34 ).

Machine learning algorithms can effectively assess surgical expertise in virtual reality simulations, improving training and education. However, interdisciplinary communication and standardized reporting practices are needed to fully realize this potential.

Negative and contradictory findings were also noted. Some studies reported technological limitations associated with the implementation of IAS, such as technical glitches and the need for substantial initial investment ( 32 , 35 ). In the study of Nykan Mirchi, et al. ( 36 ), a key criticism of artificial intelligence (AI) in education centers on the lack of transparency surrounding its decision-making processes.

The study conducted by Talan (2023) examined the performance of ChatGPT by undergraduate students in an anatomy course assessment. The study found that ChatGPT outperformed the students in the examination, highlighting its advanced capabilities in understanding and applying anatomical knowledge. The findings demonstrate the potential of AI applications, such as ChatGPT, in education, particularly in anatomy courses. The study underscores how AI tools can serve as valuable resources for learning and assessment, offering new opportunities to enhance teaching and learning in higher education ( 35 ).

However, despite the promising advancements in IAS, challenges remain in fully realizing their potential. Effective utilization of IAS requires fostering interdisciplinary communication and collaboration between computer scientists, medical professionals, and educators. Additionally, establishing standardized reporting practices across medical and computer science journals is crucial for ensuring the transparency and comparability of research findings. Addressing these challenges paves the way for the widespread adoption of IAS and further revolutionizes medical education.

Additionally, a few articles highlighted resistance from educators and students who were accustomed to traditional assessment methods ( 34 , 37 ).

Foteini Grivokostopoulou's study introduced an educational system designed to help students learn search algorithms and artificial intelligence concepts while providing automated performance assessment. The system integrates visualized animations of algorithmic operations, interactive exercises, and an automated assessment mechanism. The extended evaluation in the study showed encouraging results, with improvements in student learning outcomes and a reduction in tutors' workload for marking tasks. The automated assessment system provided a consistent and reliable grading mechanism, more effective feedback for students, and enhanced understanding of search algorithms. The research highlights the potential of the system to overcome teaching challenges in programming and algorithms while improving the efficiency of assessment and feedback processes. Overall, it demonstrates how intelligent educational systems can enhance both teaching and learning experiences ( 32 ).

Most studies, however, provided detailed statistical analyses, including descriptive statistics that summarized data on student performance before and after IAS implementation, and inferential statistics were used to determine the significance of the observed improvements ( 34 - 37 ).

The findings of this review are in the same line with other research in the field, which consistently shows that IAS can enhance learning outcomes, streamline educational processes, and provide reliable assessments. Table 3 summarizes these findings and presents the list and characteristics of the selected studies; Table 4 provides the data extraction and detailed analysis of the selected studies. However, the need for further research into long-term impacts and broader implementation challenges remains.

The findings directly address the research question: "Does the use of intelligent assessment systems (IAS) lead to improvements in educational processes, academic evaluation, and learning progress of medical science students, compared to traditional assessment methods?" The evidence strongly supports that IAS contributes positively to these areas, though challenges in implementation and acceptance must be addressed.

Key Findings from AI Interventions in Education

1. AI in Student Assessment

Several studies have focused on the use of AI to enhance student assessment processes. Wu, et al. (2021) ( 31 ) found that the intelligent computer system significantly improved student performance by providing immediate feedback. Similarly, Grivokostopoulou, et al. (2016) ( 32 ) demonstrated that an AI-driven educational system for learning algorithms improved the learning outcomes and reduced the tutors’ marking workload through automated assessment mechanisms.

2. AI in Surgical Training

The application of AI in surgical education was prominently explored. Winkler-Schwartz, et al. (2019) ( 33 ) showed that machine learning algorithms and virtual reality (VR) simulators accurately assessed surgical expertise, distinguishing between novice and expert participants. In another study, Mirchi, et al. (2020) ( 36 ) introduced an AI-based virtual operative assistant which significantly improved surgical skill acquisition in a VR environment. The findings indicated high accuracy in classifying participants based on skill levels, with specificity and sensitivity reaching over 90%.

3. AI Tutoring vs. Expert Instruction

Fazlollahi, et al. (2022) ( 34 ) compared AI tutoring systems to traditional expert instruction in teaching surgical skills. The results revealed that AI tutoring had a positive impact, improving technical performance and learning outcomes among medical students during simulated brain tumor resections. The study also highlighted the potential of AI tutoring systems to address the limited availability of expert instructors, especially in challenging times such as the COVID-19 pandemic.

4. AI for Enhancing Argumentative Reasoning

Rapanta and Walton (2016) ( 38 ) investigated using argument maps as an AI-supported tool for assessing students' argumentative reasoning. Their results indicated an improvement in critical thinking skills and argument construction, suggesting that AI tools can offer valuable pedagogical support in higher education.

5. Challenges

While the reviewed studies highlight the potential of AI in education, several challenges emerged in the implementation of these technologies for assessment purposes. A recurring theme across studies is the need for ongoing technical support and maintenance (Study 1). Intelligent systems often require specialized expertise to ensure their smooth operation and prevent technical glitches that could hinder their effectiveness.

Limited human-AI interaction represents another significant challenge. Although AI-powered systems offer the benefit of automated and immediate feedback, studies indicate that they cannot fully replace the role of teachers (Study 1, Study 10). Students, particularly those struggling with concepts or tasks, often require personalized guidance and support that AI systems alone may not be able to provide.

The need for further validation and generalization is consistently emphasized (Studies 2, 3, 4 and 5). Many AI-driven educational tools are developed and tested within specific contexts, such as specific surgical procedures (Study 4) or programming concepts (Study 9). For widespread applicability and effectiveness, these systems require validation in larger and more diverse samples across different disciplines. This includes generalizing techniques and strategies employed by specific tools to other educational areas (Study 4).

Interdisciplinary gaps and differences in reporting standards also pose challenges (Study 2). Effective integration of AI in education requires collaboration between computer scientists, educators, and subject matter experts. Bridging the communication gap between these disciplines and addressing differences in reporting practices across fields is crucial for successful adoption of AI in education.

The “black box” nature of certain AI algorithms raises concerns regarding transparency and explainability (Study 8). A lack of understanding of how AI arrives at its conclusions can limit trust in the system and hinder its effective use for feedback and improvement. Therefore, the development and implementation of explainable AI (XAI) in educational tools are essential for building confidence and fostering effective learning.

Finally, concerns about AI replacing human instructors are a potential challenge (Study 10). While AI can automate assessment tasks and provide personalized feedback, it is critical to emphasize its role as a tool to augment, rather than replace, the expertise and guidance of educators. The focus should remain on leveraging AI to enhance the teaching and learning experience, rather than solely on automation and efficiency.

Overall, the results from the included studies suggest that AI can revolutionize education, particularly in areas such as student assessment, surgical training, and the development of critical thinking skills. However, further research is needed to address existing challenges, including the integration of AI with traditional teaching methods, ensuring transparency in AI decision-making, and expanding the scope of AI applications across different educational contexts.

Discussion

The in-depth analysis of selected studies reinforces the significant role of Intelligent Assessment Systems (IAS) in enhancing medical education. These systems, empowered by artificial intelligence and intelligent systems, contribute to improved educational processes, academic assessment, and the learning progress of medical students. By leveraging machine learning algorithms and virtual reality simulators, IAS enables a more accurate and comprehensive assessment of students' clinical and cognitive skills.

One of the notable achievements of IAS implementation is the provision of immediate feedback to students. This empowers them to identify their strengths and weaknesses, leading to substantial improvements in their performance. Additionally, the ability to simultaneously assess a large number of students within a short period is a significant advantage of IAS, facilitating efficient evaluation and timely feedback ( 31 , 35 , 38 ).

The findings of the selected studies emphasize the potential of IAS to improve student performance and achieve desired learning outcomes. These systems are particularly appreciated for their ability to provide immediate feedback and reduce the instructors’ workload. By providing timely and precise feedback, IAS helps students pinpoint their areas of improvement and strengths, fostering a more targeted and effective learning process ( 15 , 32 ).

Furthermore, IAS supports the development of essential skills in medical education, such as clinical decision-making, problem-solving, and time management. The use of machine learning algorithms and virtual reality simulators not only enhances the assessment of theoretical knowledge but also provides a platform for practical skills evaluation. This comprehensive approach ensures that students are well-prepared for real-world medical scenarios ( 33 ).

The adoption of IAS in medical education offers several benefits, including enhanced learning outcomes, personalized instruction, and fair evaluation. These systems facilitate the automation of assessment processes, allowing educators to focus more on content creation and research problem-solving. The increased efficiency and effectiveness of educational processes are evident through reduced training time and costs ( 34 , 35 ).

IAS also promotes students’ self-efficacy and self-awareness by enabling them to independently tackle challenges in medical sciences and compare their results with scientific standards. Regular and transparent feedback from IAS systems helps students to continuously improve their performance and develop a deeper understanding of their learning progress ( 33 , 34 ).

Furthermore, IAS fosters interaction and collaboration among students by providing tools for group work and virtual environments. This collaborative learning environment encourages the sharing of experiences, knowledge, and feedback among peers, enhancing the overall educational experience ( 38 , 39 ).

Despite the numerous advantages, the implementation of IAS presents several challenges. A critical challenge is developing a universally accepted framework for reporting evaluation results across various dimensions of student learning progress. This framework should encompass theoretical and practical knowledge domains, diverse learning skills, problem-solving abilities, decision-making, time management, student attitudes, and even creativity ( 32 , 33 , 39 ).

The successful implementation of IAS requires careful system design, user training, and continuous technical support. Studies indicate the need for increased support and guidance for both faculty and students from system administrators. Additionally, there is a need for improved communication and collaboration between educators, students, and system developers to ensure the effective use of IAS.

Research limitations

While we aimed to provide a comprehensive and well-structured systematic review, it is important to acknowledge and address the limitations of the study. Although the used databases were comprehensive, there might be relevant studies in other databases or gray literature that were not included.The sensitivity of the search strategy to identify relevant studies could be influenced by variations in terminology and indexing practices. Also, the focus on English-language articles might have limited the inclusion of relevant studies from non-English-speaking regions.

Data-Driven Decision-Making: Utilizing data analytics enables informed decision-making to optimize the use of IAS and improve educational outcomes ( 40 ).

User-Centered Design: Involving educators and students in the design and development of IAS ensures that they meet the needs and preferences of the users, enhancing their usability and effectiveness ( 40 ).

Ethical Considerations in IAS Research

• Informed Consent: It is crucial to obtain informed consent from participants, clearly explaining how their data will be collected, stored, and used ( 41 ).

• Data Privacy and Security: Anonymizing or pseudonymizing data and implementing robust security measures are essential to protect the participants’ privacy ( 41 ).

• Human-Centered Design: Involving educators and students in the design of IAS ensures that they meet their needs and preferences. Designing IAS to be accessible to all learners, including those with disabilities, is also crucial ( 42 ).

• Educational Equity: Ensuring equal access to IAS for all students and addressing the digital divide by providing necessary support are essential ( 43 ).

• Professional Responsibility: Using AI responsibly and ethically and continuously monitoring the use of IAS to identify and address potential issues are crucial ( 44 ).

Conclusion

This systematic review highlights the crucial role of Intelligent Assessment Systems (IAS) in advancing medical education. The integration of IAS results in significant enhancements in student performance, educational processes, and academic evaluations through the application of artificial intelligence and advanced technologies.

In summary, IAS offers a promising avenue for transforming medical education, improving both the quality and efficiency of teaching and assessment. By overcoming the challenges identified and fully harnessing the potential of IAS, medical educators can greatly enhance the training and performance of future healthcare professionals. IAS offers transformative potential in medical education globally, provided challenges are addressed.

Future Research

Future research should aim to address existing limitations and investigate the long-term effects of IAS on educational outcomes. Additionally, it is essential to explore the scalability of these systems and their effectiveness across various educational environments. The long-term impacts of IAS and scalability across educational contexts require further study.

Policy Recommendations

1. Invest in Research and Development

Based on the findings of this study, substantial gaps remain in understanding the full potential and limitations of Intelligent Assessment Systems (IAS). Therefore, targeted funding should be allocated to support research efforts aiming at addressing these gaps, particularly in areas such as adaptive algorithms, data security, and equitable access. This investment would ensure continuous innovation and refinement of IAS tailored to the specific needs of medical education.

2. Establish the Standards and Guidelines

Our analysis highlights the diverse implementations and varied outcomes associated with IAS in medical education. As to consistent quality and effectiveness, this research recommends the establishment of clear standards and guidelines for their use. These standards should prioritize ethical considerations, accuracy in assessments, and adaptability to diverse learning environments, ensuring that IAS systems are both fair and reliable.

3. Support Professional Development

The findings underscore the importance of educator familiarity and competence with IAS for their successful integration into teaching practices. Providing targeted professional development opportunities, including hands-on training and ongoing support, will enable educators to maximize the benefits of IAS. This step is crucial in bridging the gap between technological advancements and practical classroom applications, ultimately enhancing both teaching and learning experiences.

Management Recommendations

Pilot Testing and Evaluation: Conducting pilot studies helps assess the effectiveness of IAS in specific contexts before widespread implementation ( 26 ).

Continuous Monitoring and Evaluation: Implementing a system for ongoing monitoring and evaluation allows for the identification of areas for improvement and optimization of IAS ( 26 ).

Acknowledgment

The authors would like to thank the Research Affairs of Abadan University of Medical Sciences for the financial support provided for this study (Grant No: 1703).

Authors’ Contributions

All authors contributed to the discussion, read and approved the manuscript, and agreed to be 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.

Conflict of interest

The authors declare that they have no conflicts of interest.

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