Medical schools and residencies are currently facing a shift in their teaching paradigm. The increasing amount of medical information and research makes it difficult for medical education to stay current in its curriculum. As patients become increasingly concerned that students and residents are "practicing" on them, clinical medicine is becoming focused more on patient safety and quality than on bedside teaching and education. Educators have faced these challenges by restructuring curricula, developing small-group sessions, and increasing self-directed learning and independent research. Nevertheless, a disconnect still exists between the classroom and the clinical environment. Many students feel that they are inadequately trained in history taking, physical examination, diagnosis, and management. Medical simulation has been proposed as a technique to bridge this educational gap. This article reviews the evidence for the utility of simulation in medical education. We conducted a MEDLINE search of original articles and review articles related to simulation in education with key words such as simulation, mannequin simulator, partial task simulator, graduate medical education, undergraduate medical education, and continuing medical education. Articles, related to undergraduate medical education, graduate medical education, and continuing medical education were used in the review. One hundred thirteen articles were included in this review. Simulation-based training was demonstrated to lead to clinical improvement in 2 areas of simulation research. Residents trained on laparoscopic surgery simulators showed improvement in procedural performance in the operating room. The other study showed that residents trained on simulators were more likely to adhere to the advanced cardiac life support protocol than those who received standard training for cardiac arrest patients. In other areas of medical training, simulation has been demonstrated to lead to improvements in medical knowledge, comfort in procedures, and improvements in performance during retesting in simulated scenarios. Simulation has also been shown to be a reliable tool for assessing learners and for teaching topics such as teamwork and communication. Only a few studies have shown direct improvements in clinical outcomes from the use of simulation for training. Multiple studies have demonstrated the effectiveness of simulation in the teaching of basic science and clinical knowledge, procedural skills, teamwork, and communication as well as assessment at the undergraduate and graduate medical education levels. As simulation becomes increasingly prevalent in medical school and resident education, more studies are needed to see if simulation training improves patient outcomes.
Background: Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. Objective: This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. Methods: Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. Results: A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students’ learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. Conclusions: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.
BACKGROUND: Qualitative research approaches are increasingly integrated into medical education research to answer relevant questions that quantitative methodologies cannot accommodate. However, researchers have found that traditional qualitative methodological approaches reflect the foundations and objectives of disciplines whose aims are recognizably different from the medical education domain of inquiry (Thorne, 2016, Interpretive description. New York, NY: Routledge). Interpretive description (ID), a widely used qualitative research method within nursing, offers an accessible and theoretically flexible approach to analysing qualitative data within medical education research. ID is an appropriate methodological alternative for medical education research, as it can address complex experiential questions while producing practical outcomes. It allows for the advancement of knowledge surrounding educational experience without sacrificing methodological integrity that long-established qualitative approaches provide. PURPOSE: In this paper, we present interpretive description as a useful research methodology for qualitative approaches within medical education. We then provide a toolkit for medical education researchers interested in incorporating interpretive description into their study design. We propose a coherent set of strategies for identifying analytical frameworks, sampling, data collection, analysis, rigour and the limitations of ID for medical education research. We conclude by advocating for the interpretive description approach as a viable and flexible methodology for medical education research.
In the wake of the novel coronavirus (COVID-19) pandemic, it is abundantly clear to all the necessity of studying the pathology and widespread health consequences associated with the virus. However, what is much less clear is the impact of COVID-19 on medical education. Already, faculty and medical students are grappling with the changes that have been made and attempting to consolidate these with their plan of career development. Changes that may seem relatively minor in comparison to the global pandemic have the potential to be drastic turning points in the career progression of many. As not much is known regarding the long-lasting impact of COVID-19 on medical education, it is therefore also necessary to record and study the full impact of the changes being made. The path to entering a successful residency has been predictable for the last few years - do well on Step 1, give conference presentations, go the extra mile in clerkships and shadowing opportunities, and have meaningful non-academic extracurricular activities - all of which designed to best demonstrate a student's knowledge, persistence, collaborative spirit, and dedication to medicine. This trajectory has been changed with COVID-19 disrupting routines in hospitals, medical schools and beyond. The replacement of in-person classes with online equivalents is an obvious necessity at this time but creates a loss of collaborative experiences that has the potential to be a significant detriment to education. Likewise, the cancellation of clerkships, which are necessary for both skill acquisition as well as for relationship building, is a serious issue which students and medical schools must now resolve. Many medical students have also lost the opportunity for personal development through conference presentations. These presentations play a large role in distinguishing applicants during the residency application process, and therefore these lost opportunities have the potential to be a serious detriment to medical students' career trajectory. While implementing technology to help resolve these issues is a unique way to help students to develop these skills, it is now necessary for medical students to demonstrate the same set of skills which they would have previously in a completely new and innovative manner. Persistence and adaptability during this time of challenge are attributes that medical students can demonstrate more readily. While every student has a personal story of how COVID-19 has impacted their education, there is no question that the impacts of COVID-19 will be felt on an extensive level. The panic in the community is palpable, and many are confused by how to proceed in the wake of COVID-19. This is no different for medical students and faculty and the questions that arise regarding medical education and their future careers.
The Carnegie Foundation for the Advancement of Teaching, which in 1910 helped stimulate the transformation of North American medical education with the publication of the Flexner Report, has a venerated place in the history of American medical education. Within a decade following Flexner's report, a strong scientifically oriented and rigorous form of medical education became well established; its structures and processes have changed relatively little since. However, the forces of change are again challenging medical education, and new calls for reform are emerging. In 2010, the Carnegie Foundation will issue another report, Educating Physicians: A Call for Reform of Medical School and Residency, that calls for (1) standardizing learning outcomes and individualizing the learning process, (2) promoting multiple forms of integration, (3) incorporating habits of inquiry and improvement, and (4) focusing on the progressive formation of the physician's professional identity. The authors, who wrote the 2010 Carnegie report, trace the seeds of these themes in Flexner's work and describe their own conceptions of them, addressing the prior and current challenges to medical education as well as recommendations for achieving excellence. The authors hope that the new report will generate the same excitement about educational innovation and reform of undergraduate and graduate medical education as the Flexner Report did a century ago.
Teaching medical professionalism is a fundamental component of medical education. The objective is to ensure that students understand the nature of professionalism and its obligations and internalize the value system of the medical profession. The recent emergence of interest in the medical literature on professional identity formation gives reason to reexamine this objective. The unstated aim of teaching professionalism has been to ensure the development of practitioners who possess a professional identity. The teaching of medical professionalism therefore represents a means to an end.The principles of identity formation that have been articulated in educational psychology and other fields have recently been used to examine the process through which physicians acquire their professional identities. Socialization-with its complex networks of social interaction, role models and mentors, experiential learning, and explicit and tacit knowledge acquisition-influences each learner, causing them to gradually "think, act, and feel like a physician."The authors propose that a principal goal of medical education be the development of a professional identity and that educational strategies be developed to support this new objective. The explicit teaching of professionalism and emphasis on professional behaviors will remain important. However, expanding knowledge of identity formation in medicine and of socialization in the medical environment should lend greater logic and clarity to the educational activities devoted to ensuring that the medical practitioners of the future will possess and demonstrate the qualities of the "good physician."
WHEN the Liaison Committee on Medical Education (LCME) was formed in 1942, a total of 67 recognized 4-year medical schools and ten 2-year (basic science) schools enrolled 22674 students. Today, one hundred twenty-five 4-year medical schools and one 2-year school enroll about 65000 students. The 50th anniversary of the LCME provides us with an occasion both to celebrate the major role the LCME has played in expanding medical education and maintaining its quality and to remind ourselves that the accrediting body faces challenges similar to those confronting the medical profession at large. Prior to 1942, the two sponsors of the LCME, the American Medical Association Council on Medical Education (AMA CME) and the Association of American Medical Colleges (AAMC), had been evaluating medical schools independently since the beginning of the 20th century. The AAMC began inspecting schools as a membership requirement in 1903; the AMA CME first classified medical schools
Simulation-based training (SBT) has emerged as a transformative approach in medical education, significantly enhancing healthcare professionals' learning experience and clinical competency. This article explores the impact of SBT, tracing its historical development and examining the various types of simulations utilized today, including high-fidelity mannequins, virtual reality environments, standardized patients, and hybrid simulations. These methods offer a safe and controlled environment for students to practice and hone technical and non-technical skills, ultimately improving patient safety and clinical outcomes. The benefits of SBT are manifold, including enhanced skill acquisition, error reduction, and the opportunity for repeated practice without risk to actual patients. Immediate feedback and structured debriefing further solidify learning, making Simulation an invaluable tool in medical education. However, the implementation of SBT is challenging. It requires substantial financial investment, specialized equipment, and trained faculty. Additionally, there are concerns about the realism of simulations and the transferability of skills to real-world clinical settings. Despite these challenges, numerous case studies and empirical research underscore the effectiveness of SBT compared to traditional methods. Looking ahead, advancements in technology, such as artificial intelligence and improved virtual reality applications, promise to enhance the efficacy and accessibility of simulation training. The integration of Simulation with other training modalities and its adoption in diverse global contexts highlight its potential to revolutionize medical education worldwide. This article affirms the crucial role of SBT in preparing the next generation of healthcare professionals and its ongoing evolution driven by technological innovations.
CONTEXT: Medical education is as much about the development of a professional identity as it is about knowledge learning. Professional identities are contested and accepted through the synergistic internal-external process of identification that is constituted in and through language and artefacts within specific institutional sites. The ways in which medical students develop their professional identity and subsequently conceptualise their multiple identities has important implications for their own well-being, as well as for the relationships they form with fellow workers and patients. OBJECTIVES: This paper aims to provide an overview of some current thinking about identity and identification with the aim of highlighting some of the core underlying processes that have relevance for medical educationists and researchers. These processes include aspects that occur within embodied individuals (e.g. the development of multiple identities and how these are conceptualised), processes specifically to do with interactional aspects of identity (e.g. how identities are constructed and co-constructed through talk) and institutional processes of identity (e.g. the influence of patterns of behaviour within specific hierarchical settings). IMPLICATIONS: Developing a systematic understanding into the processes through which medical students develop their identities will facilitate the development of educational strategies, placing medical students' identification at the core of medical education. CONCLUSIONS: Understanding the process through which we develop our identities has profound implications for medical education and entails that we adopt and develop new methods of collecting and analysing data. Embracing this challenge will provide better insights into how we might develop students' learning experiences, facilitating their development of a doctor identity that is more in line with desired policy requirements.
ISSN 1179-7258 Advances in Medical Education and Practice (AMEP) aims to present and publish research on Medical Education covering medical, dental, nursing and allied health care professional education. The journal covers undergraduate education, postgraduate training and continuing medical education including emerging trends and innovative models linking education, research, and health care services. The main focuses are curriculum development, teaching methodology, student assessment, curriculum evaluation, career planning, teachers’ training and continuing professional development. AMEP is published as a peer-reviewed, open-access journal to provide information on education and research to be immediately available to learners, educators, practitioners, policymakers and other stakeholders who can access and utilize the evidences to improve the quality of education and services.
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. OBJECTIVE: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. METHODS: We used 2 sets of multiple-choice questions to evaluate ChatGPT's performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT's performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. RESULTS: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT's answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and NBME-Free-Step2 (P=.001) data sets, respectively. CONCLUSIONS: ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT's capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.
The AMACA project (Astronomy education with a Multi-sensory, Accessible, and Circular Approach) develops multi-sensory activities for accessible education and engagement in astronomy. Despite promising innovations, existing resources are often poorly documented, designed for one-time events, expensive, and lack interdisciplinary collaboration, user testing, and broad dissemination. AMACA addresses these challenges by creating multi-sensory activities for education and outreach, with a particular focus on accessibility for people with sensory disabilities. A circular approach informs its educational structure: (1) a PhD course on multi-sensory astronomy outreach develops hands-on activities with the support of astronomers, psychologists, and organizations for the visually impaired and the deaf; (2) PhD candidates teach High School (HS) students how to deliver the activities; (3) HS students lead the activities at the Astronomy Festival "The Universe in All Senses"; (4) HS students train teachers to implement the activities in their classrooms. AMACA also develops tools to guide project development and track participants' learning. Key findings show improved communication and accessi
Contribution: This article analyzes the learning effectiveness of a virtual educational escape room for teaching software engineering and compares this activity with traditional teaching through a randomized controlled trial. Background: Educational escape rooms have been used across a wide variety of disciplines at all levels of education and they are becoming increasingly popular among teachers. Nevertheless, there is a clear general need for more robust empirical evidence on the learning effectiveness of these novel activities and, particularly, on their application in software engineering education. Research Questions: Is game-based learning using educational escape rooms more effective than traditional lectures for teaching software engineering? What are the perceptions of software engineering students toward game-based learning using educational escape rooms? Methodology: The study presented in this article is a randomized controlled trial with a pre-and post-test design that was completed by a total of 326 software engineering students. The 164 students belonging to the experimental group learned software modeling by playing an educational escape room whereas the 162 student
The gap between theory and practice is well-documented in educational research. Physics teachers' willingness to apply research findings in practice may be influenced by a sceptical attitude towards science education research. This study explores physics teachers' perspectives on science education research, with a particular focus on potential scepticism towards the discipline. A two-step mixed-methods approach was employed: (1) Interviews with a purposeful sample of 13 experienced physics teachers for a first exploration of attitudes towards physics education research, and (2) a quantitative survey of 174 physics teachers to examine, among other aspects, the previously observed attitudes in a larger sample and to identify teacher profiles using latent profile analysis. The interview study revealed both sceptical and non-sceptical attitudes towards physics education research, including some that fundamentally questioned its practical value. Based on the survey data and latent profile analysis, four distinct teacher profiles differing in their level of scepticism towards science education research were identified. While one profile is highly sceptical, the other three exhibit a mix
This entry introduces educational games in secondary schools. Educational games include three main types of educational activities with a playful learning intention supported by digital technologies: educational serious games, educational gamification, and learning through game creation. Educational serious games are digital games that support learning objectives. Gamification is defined as the use of "game design elements and game thinking in a non-gaming context" (Deterding et al. 2011, p. 13). Educational gamification is not developed through a digital game but includes game elements for supporting the learning objectives. Learning through game creation is focused on the process of designing and creating a prototype of a game to support a learning process related to the game creation process or the knowledge mobilized through the game creation process. Four modalities of educational games in secondary education are introduced in this entry to describe educational games in secondary education: educational purpose of entertainment games, serious games, gamification, and game design.
Contribution: This article analyzes the learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however, it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research Questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test, and questionnaire) with the purpose of comparing the effectiveness o
Modern Education is not \textit{Modern} without AI. However, AI's complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent's income greatly influences a child's education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI tools. Our research uncovered many complexities linked to parental income and offered reasonable explanations for these decisions. However, we also found biases in AI that go against what we want from AI in education: clear transparency and equal access for everyone. These biases can impact families and children's schooling, highlighting the need for better AI solutions that offer fair opportunities to all. This chapter tries to shed light on the complex ways AI operates, especially concerning biases. These are the foundational steps towards better educational policies, which include using AI in ways that are more reliable, accountable, and beneficial for everyone involved.
This chapter introduces the AI & Data Acumen Learning Outcomes Framework, a comprehensive tool designed to guide the integration of AI literacy across higher education. Developed through a collaborative process, the framework defines key AI and data-related competencies across four proficiency levels and seven knowledge dimensions. It provides a structured approach for educators to scaffold student learning in AI, balancing technical skills with ethical considerations and sociocultural awareness. The chapter outlines the framework's development process, its structure, and practical strategies for implementation in curriculum design, learning activities, and assessment. We address challenges in implementation and future directions for AI education. By offering a roadmap for developing students' holistic AI literacy, this framework prepares learners to leverage generative AI capabilities in both academic and professional contexts.
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex medical scenarios. In this study, we introduced Test-Time Generative Augmentation (TTGA), a novel augmentation strategy specifically tailored for medical image segmentation at inference time. Different from conventional augmentation strategies that suffer from excessive randomness or limited flexibility, TTGA leverages a domain-fine-tuned generative model to produce contextually relevant and diverse augmentations tailored to the characteristics of each test image. Built upon diffusion model inversion, a masked null-text inversion method is proposed to enable region-specific augmentations during sampling. Furthermore, a dual denoising pathway is designed to balance precise identity preservation with controlled variability. We demonstrate the efficacy of our TTGA through
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e.g., Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensiti