Technology-enhanced learning (TEL) has revolutionized the way students learn. In health professions education, TEL is particularly impactful as it ensures that future healthcare professionals are well-prepared to meet the demands of modern medical practice. Given the continuous advancements in educational technology, there is a pressing need to examine the integration of these technologies in this field. Therefore, this chapter reviews the current trends and applications, including artificial intelligence, smart classrooms, extended realities, digital game-based learning, mobile learning applications, metaverses, the Internet of Medical Things, robotic telepresence, telemedicine training, and virtual simulations. Doing so guides educators, policymakers, and technology developers in creating more engaging, efficient, and inclusive educational environments. Overall, the chapter underscores the necessity of ongoing research and thoughtful technology integration to prepare competent, knowledgeable, and adaptable health professionals for the ever-changing demands of the healthcare field.

The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the overall academic performance evaluation. To address this gap, this study employed cognitive diagnostic modeling (CDM) to profile the skill mastery of programming students. An empirical analysis was conducted to select the most appropriate model for the data, and the linear logistic model (LLM) was determined to be the best fit. Final examination results from 308 information technology (IT) and 279 computer science (CS) students were analyzed using the LLM. Unfortunately, findings revealed that programming students exhibited proficiency primarily in code tracing and language proficiency but displayed deficits in theoretical understanding, logical reasoning, and algorithmic thinking. From a practical standpoint, this deficiency in fundamental skills sheds light on the factors contributing to academic failures and potentially eventual dropout in programming education. When comparing the student population by academic program, CS students demonstrated superior mastery compared to their IT counterparts, although both groups exhibited a lack of mastery in code tracing. These deviations underscore the pressing need for tailored educational strategies that address the unique strengths and weaknesses of each student group. Overall, this study offers valuable insights into programming education literature and contributes to the expanding application of CDM in educational research.

The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.

Addressing the worldwide crisis of inadequate physical education (PE) programs requires immediate attention. Despite the advocacy of international organizations like UNESCO and WHO, there still needs to be a significant gap in understanding the effectiveness of PE initiatives globally. Cultural, socio-economic, and policy differences further complicate evaluating and improving these programs. More comprehensive research is needed to promote academic achievement, well-being, and overall health. This is where Global Innovations in Physical Education and Health comes in, a groundbreaking solution poised to revolutionize PE on a global scale. This innovative book serves as a beacon of hope by exploring diverse teaching strategies and creative methods worldwide. Bridging critical research gaps empowers policymakers, educators, researchers, administrators, and health professionals with actionable insights to enhance the quality and inclusivity of PE programs. With its comprehensive coverage of topics such as adaptive PE, nutritional education, and global health initiatives, this book provides a roadmap for transforming PE into a catalyst for holistic health and lifelong well-being.

PE often finds itself overlooked in the broader discourse on educational innovation, despite its crucial role in fostering lifelong health and well-being. Unlike subjects that easily attract attention due to their academic prestige or technological allure, PE is sometimes seen as secondary, relegated to the sidelines of educational reform. Yet, in an age where global health challenges such as obesity, mental health disorders, and sedentary lifestyles are increasingly prevalent (World Health Organization, 2020), the significance of PE cannot be understated. The Global Innovations in Physical Education and Health book seeks to challenge this perception by highlighting the transformative potential of innovative approaches in PE and health education (Garcia, Lopez Cabrera, et al., 2023). It brings together 69 experts from nine countries—Indonesia, Philippines, China, Ireland, United Arab Emirates, Portugal, India, USA, and Canada—to explore how these innovations are reshaping the way we teach, experience, and benefit from PE, making it a field deserving of as much attention and innovation as any other educational domain. This book serves as a crucial resource in guiding the development of more effective, inclusive, and culturally aware health and PE programs. It sets the foundation for actionable teaching and learning models in various educational and cultural contexts. The coverage includes insights into how different countries approach PE, the integration of health education within these programs, and their impact on student health and academic achievement.

The integration of AI technologies like ChatGPT has transformed academic research, yet substantial gaps exist in understanding the implications of AI-generated non-existent references in literature searches. While prior studies have predominantly focused on medical and geography fields using descriptive statistics, a systematic investigation into ChatGPT 4.0’s effectiveness in generating accurate references within the realm of science and technology education remains unexplored, highlighting a significant dearth of research in this critical area. This study, therefore, investigates the reliability of AI-generated references in academic writing utilizing ChatGPT 4.0. Employing a non-experimental correlational design, the research examines the impact of prompt specificity on citation accuracy across various types of prompts, including general, specific, methodological, review, and interdisciplinary prompts. The findings indicate that specific, review, and interdisciplinary prompts correlate positively with accurate references, while general prompts frequently result in non-existent references. Visualizations, including a confusion matrix and precision-recall curve, illustrate the model’s performance. Ultimately, the study underscores the necessity of well-structured prompts to enhance reference quality and cautions against AI-induced hallucinations that produce non-existent references, which can significantly undermine research credibility.

This study examined factors driving the adoption of generative artificial intelligence tools like ChatGPT for research writing through an integrated framework combining the Technology Acceptance Model, Task Technology Fit, and Trust in Specific Technology. Responses from 564 researchers in 12 countries were analyzed using a structural equation modeling approach. Intriguingly, perceived usefulness and ease of use were insignificant despite being considered the strongest predictors of behavioral intention in countless studies. Instead, researchers prioritize trusting beliefs and the compatibility between a technology and a task when considering its use. It was also found that trust in the technology has greater explanatory power than task-technology compatibility, and this trust is influenced by beliefs that ChatGPT is a socially and academically accepted tool for manuscript writing. Overall, this study contributes new insights for researchers, funding bodies, publishers, policymakers, and the academic community as they navigate the evolving role of AI in scholarly writing.

This chapter provides an overview of the current state of medical imaging methods in body composition analysis. It advocates a holistic approach that combines the strengths of different approaches and addresses their limitations. We discuss the importance of using standardized protocols to improve the accuracy of body composition studies across populations and settings. By examining the capabilities and limitations of imaging modalities such as DEXA, MRI, CT, and ultrasound, we emphasize the need for a multidimensional approach to obtain body composition emphasis on complete understanding.

Teaching in the metaverse presents a dynamic frontier for educational innovation. Avatars, serving as digital representations of teachers, play a pivotal role in shaping virtual learning experiences. This study explores the impact of avatar design and behavioral realism on student perceptions of credibility and teaching effectiveness in avatar-mediated environments. True experimental research with a 2 × 2 factorial design was conducted involving students from three campuses. Across all experimental conditions, students consistently favored realistic avatars over cartoonish ones. A crisscross pattern emerged in relation to behavioral realism. Cartoonish avatars exhibiting realistic behaviors received higher ratings for instructor credibility but not for teaching effectiveness, whereas realistic avatars with the same gestures received higher ratings for teaching effectiveness but not for instructor credibility. From an educational standpoint, leveraging realistic avatars with authentic behaviors holds great promise for enhancing the teaching and learning experiences in the metaverse. Overall, this study contributes to the growing body of literature on educational metaverse and avatar-mediated teaching and learning by shedding light on the importance of avatar design and behavioral realism in shaping student perceptions and experiences.

The emergence of generative AI tools like ChatGPT has sparked investigations into their applications in teaching and learning. In computer programming education, efforts are underway to explore how this tool can enhance instructional practices. Despite the growing literature, there is a lack of synthesis on its use in this field. This rapid review addresses this gap by examining the current literature to outline research trends, assess how it supports teaching and learning processes, and discern the issues that emerge from its application in programming instruction. A total of 107 documents disseminated across 81 distinct sources and authored by 394 contributors were identified. The review adopted a broad and inclusive approach, selecting literature based on relevance to ChatGPT's application in programming education and encompassing studies from diverse settings and methodologies. Results highlight applications such as personalized tutoring, knowledge reinforcement, instructional material creation, source code generation, immediate feedback, and assessment support. However, its use also introduces challenges such as academic dishonesty, ethical dilemmas, diminished critical thinking, overdependence on ChatGPT, and various technical limitations. Considering these findings, a balanced approach to the utilization of ChatGPT in programming education is essential. Implications and recommendations have been provided to guide policymakers, curriculum designers, teachers, and students in harnessing the benefits of this technology while mitigating potential challenges.