UNASSIGNED: The rapid advancement of technology and the integration of innovative medical devices are significantly transforming medical education. This review examines the impact of these changes and the importance of adapting educational strategies to leverage these advancements.
UNASSIGNED: This narrative review employs a qualitative approach. From an initial pool of 294 articles, researchers conducted independent screenings and identified 134 studies relevant to innovations in technology and their impact on medical education. Following a comprehensive review and consensus, studies deemed to be of low relevance were excluded, resulting in a final selection of 74 articles. An expert panel discussion was held, and the study concludes with a final section that presents the findings and offers brief, clear recommendations.
UNASSIGNED: This study indicates that the utilization of Innovative medical technologies has the potential to enhance learning outcomes. The use of simulations allows students to engage in hands-on practice without risking patient harm. Mobile devices afford students uninterrupted access to educational resources, thereby enabling efficient learning. Artificial intelligence (AI) has the potential to personalize education, enhance diagnostic skills, and foster critical thinking. Further research in this field has the potential to yield significant insights.

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI\'s ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools\' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy\'s factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT\'s accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT\'s current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students\' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes.

The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs. This research presents an AI-driven adaptive learning platform implemented across four different courses and 300 students at a university in Faisalabad-Pakistan. A controlled experiment compares student outcomes between those using the AI platform and those undergoing traditional instruction. Quantitative results demonstrate a 25 % improvement in grades, test scores, and engagement for the AI group, with a statistical significance of a p-value of 0.00045. Qualitative feedback highlights enhanced experiences attributed to personalized pathways. The DL analysis of student performance data highlights key parameters, including enhanced learning outcomes and engagement metrices over time. Surveys reveal increased satisfaction compared to one-size-fits-all content. Unlike prior AI research lacking rigorous validation, our methodology and significant results deliver a concrete framework for institutions to implement personalized, AI-driven education at scale. This data-driven approach builds on previous attempts by tying adaptations to actual student needs, yielding measurable improvements in key outcomes. Overall, this work empirically validates that AI platforms leveraging robust analytics to provide customized and adaptive learning can significantly enhance student academic performance, engagement, and satisfaction compared to traditional approaches. These findings have insightful consequences for the future of higher education. The research contributes to the growing demand for AI in education research and provides a practical framework for institutions seeking to implement more adaptive and student-centric teaching methodologies.

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Smart learning environments (SLEs) have been developed to create an effective learning environment gradually and sustainably by applying technology. Given the growing dependence on technology daily, SLE will inevitably be incorporated into the teaching and learning process. Without transforming technology-enhanced learning environments into SLE, they are restricted to adding sophistication and lack pedagogical benefits, leading to wasteful educational investments. SLE research has grown over time, particularly during the COVID-19 pandemic in 2020-2021, which fundamentally altered the \"landscape\" of technology use in education. This study aims to discover how the stages of SLE transform from time to time by applying two bibliometric analysis approaches: publication performance analysis and science mapping. The dataset was created by extracting bibliometric data from Scopus, including 427 articles, 162 publication sources (journals and proceeding), and 1080 authors from 2002 to 2022. Three kinds of SLE research subjects were identified by keyword synthesis: SLE features, technological innovation, and adaptive learning systems. Adaptive learning and personalized learning are consistently used interchangeably to demonstrate the significance of supporting the diversity of student and teacher conditions. Learning analytics, essential to employing big data technology for educational data mining, is a new theme being considered increasingly in the future to achieve adaptive and personalized learning. The 20-year SLE research milestone, broken down into five stages with various focuses on goals and served as the foundation for creating a maturity model of SLE.

Personalization of learning is an educational strategy rooted in metacognition and is significant in academic training. This is especially true in medical contexts. This study explored the relationship between the metacognitive profile of students of human anatomy, the classification of questions according to their difficulty, and the anatomical domain. It also covered the integration of educational technologies to create personalized learning environments. The identification of metacognitive profiles (\"Active\", \"Pragmatic\", \"Theoretical\", and \"Reflective\") has been highlighted as a critical influence on students\' responses to different pedagogical approaches. Personalized adaptation based on these profiles has shown potential for improving grades and increasing student satisfaction and engagement with learning. The results revealed variations in student performance in relation to different pedagogical approaches, learning units, and evaluation modalities. The \"Experience\" evaluation modality, personalized according to metacognitive profiles, level of competence, and learning objectives, resulted in higher average scores. However, there was significant variability in the results. Those findings confirm the effectiveness of metacognitive adaptation in improving academic performance. Furthermore, they provide a solid basis for formulating personalized and effective pedagogical strategies in medical education. They recognize the influence of metacognitive profiles on student performance and contribute to advancing medical pedagogy.

The knowledge and application of pharmacology is essential for safe prescribing and administration of drugs. In this narrative review, the challenges to pharmacology education in the medical curricula were broadly identified to include issues around content and pedagogies. The increasing number of approved drugs and drug targets, expanding field of pharmacology and the often-changing treatment guidelines and board-defined competencies can make pharmacology education in the medical curriculum daunting. There has been a consensus around the deployment of innovative medical curricula with emphasis on vertical and horizontal integration. This strategy, effective as it has been, presents new challenges to pharmacology education. As a discipline often perceived by students to be hard-to-learn, the future of pharmacology education must include heavy reliance on active learning strategies. The continuing utilization of problem-based, team-based and case-based learning can be complemented with personalized learning which aims to identify the learning gaps in individual students. Technology-inspired student engagement can foster pharmacology learning and retention. Early exposure to pharmacology from premedical preparation through an enduring across-the-level integration can be an effective way to enhance pharmacology learning in the medical curricula.

Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.

UNASSIGNED: Improving medical student placements relies on being able to reliably evaluate how students experience clinical learning environments. The Undergraduate Clinical Education Environment Measure (UCEEM) is an increasingly used validated tool designed to allow such evaluations. This study aims to further characterize how the UCEEM relates to qualitative evaluation.
UNASSIGNED: Students on placement at one hospital were invited to complete the UCEEM before and after the implementation of an innovative new placement structure. Additionally, focus groups were employed to collect qualitative data on their experiences. a novel protocol to triangulate the output of the UCEEM with the qualitative data was developed.
UNASSIGNED: The UCEEM showed good internal consistency (Cronbach\'s Alpha 0.79-0.91) and internal correlation. Implementation of the intervention created significant improvements in the overall UCEEM scores (P = .008) and in the \"Learning in and through work and quality of supervision\" (P = .048), \"Preparedness for student entry\" (P = .033) and \"Workplace interaction patterns and student inclusion\" (P = .039) domains. The triangulation of qualitative data with UCEEM output showed that the UCEEM allowed evaluation of some perceptions not reached through open questioning. However, mixed interpretations of UCEEM items by students led to the conflation of themes and challenges in deriving the meaning behind the score. This appeared to be the case for 14 of the 24 UCEEM items.
UNASSIGNED: This investigation adds to the literature supporting the UCEEM as a validated tool. It also elucidates the limitations and relationships to qualitative data that investigators need to be aware of in its use.

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