Once confined to science fiction and speculative futures, generative artificial intelligence (GenAI) has swiftly entered the lecture halls of modern medical education. Despite its expanding use, a synthesis of its implementation, limitations, and educational value remains underexplored. This review aims to critically examine current applications, identify pedagogical pitfalls, and delineate future trajectories for GenAI in medical training. Key innovations include AI-driven content generation tailored to curricular benchmarks, automated assessments with real-time diagnostic feedback, and immersive virtual patient simulations replicating complex pathophysiologies. Additional advances span multilingual knowledge translation, anatomically precise surgical training environments, and adaptive learning systems powered by intelligent tutoring frameworks. As discussed herein, GenAI holds transformative potential for advancing clinical competence in an evolving medical landscape—provided its integration is evidence-based, ethically sound, and educationally coherent.

Philippine is one of the countries near in the Pacific Ring of Fire. In recent years, several moderate to high seismic activities happened that leads to casualties, deaths and damages in different structures. Manila is the capital of the Philippines with a population of almost 1.8 million. Many structures have been considered as old and unsafe and with an impending earthquake, it is essential to rehabilitate these structures. The imminent danger of the West Valley Fault when it moves is known throughout the metro manila and other neighbouring regions. The damage and casualties that will sustain from the possible 7.2 magnitude earthquake is fatal. Conducting mitigation programs is critical for it will greatly benefit the government and the people. Rapid Visual Screening (RVS) is an effective and efficient way of assessing the building’s structural integrity. This methodology is used to assess the structure’s seismic risk by visual observation of the exterior and interior of the buildings and a data collection form. RVS was applied in 26 hospital building’s located in Manila and the outcome of the assessment has shown that only 6 hospital buildings proved to be seismically adequate when using the level 1 data collection form. RVS is an effective tool in providing initial insight in the building’s vulnerability to seismic event.

Hydrogen production via water electrolysis faces significant commercialization challenges due to the high cost and scarcity of iridium (Ir). Reducing Ir loading while maintaining high catalytic performance is critical for advancing proton exchange membrane (PEM) water electrolyzers. To address these challenges, this study introduces Ni@Ir core-shell nanowires (CSNW) as a cost-effective catalyst for enhancing Ir utilization in oxygen evolution reaction (OER). Ni@Ir-CSNW features an amorphous Ir shell coating crystalline Ni nanowires, achieving a high BET surface area of 417 m2 g−1 and an electrochemical surface area (ECSA) of 324 m2 g−1. With a reduced Ir loading of 22 wt.%, Ni@Ir-CSNW delivers remarkable performance, including a mass activity of 9.4 A mg−1 which is a 35-fold increase compared to conventional Ir nanoparticle (Ir-NP) catalysts. Ni@Ir-CSNW achieves an overpotential of 260 mV at 10 mA cm−2 and a favorable Tafel slope of 54 mV dec−1. Stability tests further demonstrate 68% retention of mass activity after 100 cycles. This work presents an effective strategy to enhance catalyst performance while reducing Ir usage, contributing to more sustainable and economically viable PEM water electrolysis systems.

As artificial intelligence (AI) becomes increasingly integrated into educational contexts, they present new challenges to traditional assessment methods. A particularly pressing issue is academic dishonesty, which undermines learning authenticity and the credibility of educational institutions. With generative AI tools like ChatGPT making it easier for students to produce automated answers, educational assessments are at risk of measuring AI capabilities rather than students' actual knowledge. Thus, this chapter explores a range of strategies designed to adapt assessment practices in response to the influence of AI in education. These strategies offer actionable frameworks to support authentic learning and uphold academic integrity. Additionally, the chapter highlights future research directions to guide further adaptation of educational policies and practices. Given the rapid integration of AI in the education sector, this chapter provides sensible insights that reinforce the importance of integrity-focused reforms in sustaining meaningful educational outcomes in an AI-driven world.

As generative AI tools become increasingly integrated into educational practice, its use among pre-service teachers is often accompanied by hesitation and discomfort. This chapter examines the phenomenon of AI shaming among teacher education students—the stigma and reluctance to disclose AI tool use due to perceived threats to academic authenticity. Drawing on classroom insights and student reflections, it explores how social norms, institutional pressures, and identity formation shape this behavior. These experiences reveal the deep tension between embracing technological innovation and maintaining traditional standards of academic merit. The chapter highlights the implications for digital literacy, professional development, and ethical technology integration. It calls for a shift in narrative, framing AI not as a shortcut but as a tool for innovation. Actionable strategies for educators and institutions are proposed to foster open, reflective, and supportive environments for responsible AI use in teacher education.

In an era marked by rapid technological advancement, protecting the intellectual property (IP) of educational innovations has become more critical than ever. This chapter examines the intersection of educational innovation, artificial intelligence (AI), and IP protection. Patents, which safeguard the technical and functional aspects of inventions, are crucial for protecting these advancements amid rapid technological disruptions. As discussed in the chapter, several challenges are posed by AI in generating and managing IP, including the need to redefine inventorship, address skill obsolescence, and ensure equitable IP frameworks. Despite the importance of addressing these issues to foster innovation, they remain underexplored in the existing literature. Therefore, this chapter calls for a reassessment of existing legal and procedural frameworks to adapt to the evolving IP landscape and sustain the integrity of educational innovations. Overall, this chapter aims to contribute to the development of robust strategies for safeguarding educational innovations in an AI-driven era.

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.