Purpose: This study aimed to examine how physical education (PE) students engage with fitness content on TikTok. Methods: The evaluation involved 597 students enrolled in a PE 1 course across three campuses of a prominent university. Results: Findings show that students primarily watch TikTok videos for entertainment, with male students also seeking motivation and social interaction, while female students look for escape, advice, and guidance. Engagement is highest for videos featuring body transformations, fitness tips, and motivational content, with a tendency to apply learned exercises, tips, and nutrition education. Body transformations and motivational videos effectively arouse emotions and elicit affective responses. Conclusion: This research highlights diverse motivations and impacts of fitness content on TikTok among PE students, contributing to the literature on social media usage and offering insights for enhancing instructional practices in PE and understanding digital media interaction.

Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its potential use and future impact on education. Adopting the methodology of collective writing as an inquiry, this study reports on the participating educators’ perceived grey areas (i.e. issues that are unclear and/or controversial) and recommendations on future research. The grey areas reported cover decision-making on the use of GenAI, AI ethics, appropriate levels of use of GenAI in education, impact on learning and teaching, policy, data, GenAI outputs, humans in the loop and public–private partnerships. Recommended directions for future research include learning and teaching, ethical and legal implications, ownership/authorship, funding, technology, research support, AI metaphor and types of research. Each theme or subtheme is presented in the form of a statement, followed by a justification. These findings serve as a call to action to encourage a continuing debate around GenAI and to engage more educators in research. The paper concludes that unless we can ask the right questions now, we may find that, in the pursuit of greater efficiency, we have lost the very essence of what it means to educate and learn.

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.