The integration of artificial intelligence (AI) into engineering education has emerged as a transformative force, offering innovative tools to enhance teaching, learning, and administrative processes. This study presents a systematic review of the current landscape, focusing on the AI technologies application, the regulatory frameworks, and the challenges encountered in engineering education. The findings reveal how AI can improve student learning outcomes, personalize educational experiences, and automate complex processes. The review also addresses critical issues, such as ethical considerations and the imperative for regulatory compliance. Furthermore, it identifies key barriers to adoption, such as technological limitations and the preparedness of educators and students to embrace AI-powered solutions. This study provides a comprehensive understanding of the potential and limitations of AI in engineering education, offering actionable insights for educators, policymakers, and stakeholders aiming to foster effective and ethical AI integration in academic settings.

As artificial intelligence (AI) continues to transform the demands of the global workforce, technical education must evolve to meet these emerging challenges. This chapter examines the integration of AI in technical education with an emphasis on the critical need for modern infrastructure and technical expertise. It highlights the importance of investing in facilities such as AI-equipped laboratories, reliable internet, and educator training programs to foster innovation and personalized learning. Collaboration between educational institutions and industry is explored as a means to bridge the gap between academic theory and real-world applications. Additionally, the chapter advocates revising curricula to combine AI literacy with technical skills, alongside critical thinking and adaptability, to meet evolving workforce demands. It concludes with a call for educators, policymakers, and institutions to prioritize inclusive, forward-thinking strategies to modernize technical education and ensure equity in access and opportunities.

The integration of artificial intelligence (AI) in education rapidly transforms the teaching and learning process. Recent systematic reviews have shown an increase in research studying the opportunities and challenges associated with AI in education. This trend reflects a growing recognition of its potential to revolutionize educational practices. However, there are also growing concerns and issues with skill obsolescence leading to job displacement, algorithm bias, and misuse of AI for academic dishonesty. As educational institutions increasingly rely on AI to enhance academic outcomes, proactively addressing these challenges ensures the ethical and responsible use of AI in education. Pitfalls of AI Integration in Education: Skill Obsolescence, Misuse, and Bias offers a targeted exploration of the critical challenges and concerns that arise as AI becomes more embedded in educational systems. Focusing on emerging issues, it addresses the gaps in current research and practice, shedding light on the ethical, practical, and pedagogical dilemmas that educators, students, and institutions face. Covering topics such as school infrastructure, critical academic skills, and intellectual property protection, this book is an excellent resource for educators, school administrators, policymakers, professionals, researchers, academicians, and more.

The Pitfalls of AI Integration in Education: Skill Obsolescence, Misuse, and Bias book is a response to the research gaps and unanswered questions. It is a deliberate shift away from techno-utopianism toward a grounded, critical engagement with AI's role in shaping educational futures. While much of the prevailing discourse celebrates possibility, we have chosen to examine peril: the risk of skill atrophy when generative tools supplant creative labor, the encroachment of surveillance technologies under the guise of pedagogical support, and the entrenchment of biases within algorithmic systems that claim neutrality while operationalizing historical inequities. Our contributors span the domains of education, computer science, ethics, policy, and cognitive science, offering a multidisciplinary interrogation of the unintended, often unanticipated, consequences of AI integration in classrooms, curricula, and institutional systems. We are not alarmists but realists. We do not advocate abandoning AI, nor do we harbor nostalgia for a pre-digital past. Rather, we argue for a more discerning adoption—one anchored in pedagogical intent, procedural transparency, and an unwavering commitment to human dignity. In many ways, this book could be read as a companion to the emerging genre of speculative nonfiction—not because it forecasts distant futures, but because it interrogates the ones currently under construction, often without democratic deliberation or ethical guardrails. If history and speculative fiction alike have taught us anything, it is that technological progress, when left unchecked, tends to obscure the deeper values at stake. We invite you, therefore, to read these chapters not merely as a critique but as a provocation—to think more deeply, act more responsibly, and imagine more boldly the kinds of educational futures we truly want to build.

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 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.

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.

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.