Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R2 > 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery.

Mathematics is a subject that often feels distant from everyday life, yet its logic quietly shapes the world around us. As learners continue to question its practical significance, there is a growing need to rethink mathematics education. Recently, the emergence of artificial intelligence (AI) has opened new possibilities for transforming how mathematics is taught and learned. This chapter aims to examine the emerging role of AI in mathematics education by synthesizing current tools, identifying prevailing trends, and exploring transformative applications. Using a narrative review supported by expert-informed synthesis, several themes are identified that reflect how AI are reshaping instructional practices, learner engagement, and pedagogical design. The discussion integrates illustrative examples of AI tools to highlight their instructional relevance and underlying mechanisms. The chapter concludes by reflecting on a redefined landscape for mathematics education, where technology transforms instructional practices, learner experiences, and the development of mathematical thinking.

The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) into cybersecurity education offers a significant opportunity to customize learning, improve engagement, and connect theoretical concepts with practical applications. This study presents the Dynamic Integration and Optimization of NetCyber Activities (DIONA) system which is an AI-enhanced educational tool developed from the NetFusion Learning Academy (NLA) to tackle ongoing issues in conventional cybersecurity education, including restricted adaptability, absence of real-time feedback, and inadequate practical skill application. The study employs a systematic methodology based on four distinct objectives: (1) to assess the efficacy of NLA in improving student learning across five critical domains— knowledge retention, practical skills, engagement, conceptual understanding, and problem-solving; (2) to examine student and faculty perceptions of its educational value; (3) to develop the ACTIVE AI Framework for AI-driven pedagogy; and (4) to create and validate DIONA as an AI/ML-based experiential learning platform. Statistical and thematic analyses indicated that although NLA effectively enhances knowledge and engagement, deficiencies persist in practical skills and problem-solving, necessitating the incorporation of AI-powered tools. The ACTIVE AI Framework and DIONA system offer customized learning trajectories, AI-generated feedback, and immersive simulations that correspond with authentic cybersecurity challenges. Results endorse the significance of intelligent learning analytics and specialized AI systems in transforming technical education and equipping students for changing digital environments.

This pilot study investigates the adoption trends and challenges of artificial intelligence (AI) and machine learning (ML) in the telecommunications sector, focusing on network analytics and security. Using a two-part design—literature review and survey-based statistical analysis—the study provides early insights into industry perceptions and readiness. With data from 31 telecom professionals in a leading Philippine service provider, results reveal a low level of current implementation but high expectations for future adoption. Despite the small sample size, which limits generalizability, this study contributes a foundational analysis of key drivers and obstacles of AI/ML adoption. It highlights the importance of workforce upskilling, integration strategies, and stakeholder engagement in supporting AI readiness within telecom organizations.

Artificial intelligence (AI) is becoming a disruptive force in digital marketing and advertising in the Philippines, transforming how brands plan, execute, and engage with their audiences. This study explores the impact of AI technologies such as automated targeting, personalized content delivery, and campaign optimization on industry practices and professional skillsets. Using the Task Technology Fit (TTF) Model and sentiment analysis, the research identifies a growing dependence on AI tools alongside significant challenges. These include a shortage of AI-related training opportunities, limited institutional support, and ethical concerns related to privacy and algorithmic bias. Despite these obstacles, there is cautious optimism among marketing professionals regarding AI’s potential to enhance creativity, efficiency, and strategic decision-making. The findings emphasize the importance of developing human-AI collaboration frameworks, strengthening ethical standards, and promoting AI literacy across sectors. Collaboration among educational institutions, industry leaders, and policymakers is essential to building a resilient and future-ready advertising workforce in the Philippines.

The integration of Artificial Intelligence (AI) is reshaping digital marketing in the Philippines by enhancing communication technology, enabling precise targeting, automating processes and delivering personalized experiences. This study examines how AI adoption aligns with multimedia education, educational technology and the growing need for sustainability literacy in the digital age. Using the Diffusion of Innovations theory and word cloud analysis, the research uncovers a significant gap between AI's capabilities and the readiness of Filipino digital marketers. Barriers such as limited access to digital resources, lack of formal training and unresolved ethical concerns like data privacy and algorithmic bias hinder responsible implementation. These challenges highlight the importance of equipping digital media and multimedia arts students with AI-related competencies and ethical awareness to remain relevant in the evolving industry. Strengthening collaboration between academic institutions, industry leaders and policymakers is essential to ensure inclusive and responsible AI integration. By investing in accessible digital learning tools, ethical frameworks and continuous skill development, the Philippine digital marketing ecosystem can achieve sustainable innovation while promoting equitable use of emerging technologies.

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.

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.

With the increasing popularity of artificial intelligence (AI) applications in medical practices, the integration of AI technologies into medical education has garnered significant attention. However, there exists a noticeable research gap when it comes to providing comprehensive guidelines and recommendations for its successful integration into this domain. Addressing this research gap is crucial as the responsible and effective incorporation of AI in medical education not only ensures that current and future healthcare professionals are well-prepared for the demands of modern medicine but also upholds ethical standards, maximizes the potential benefits of AI, and minimizes potential risks. The objective of this chapter is to fill this gap by offering practical tips and actionable insights for incorporating AI into medical education, encompassing practical, ethical, pedagogical, and professional implications. Consequently, it equips medical educators and learners alike with the knowledge and tools necessary to navigate the evolving landscape of medical education in the age of AI.

Energy consumption significantly increased in recent decades, notably at the household level, due to economic development, rising population, and technological advancements. To address this sustainability concern, peer-to-peer energy sharing service (P2PESS) is introduced as a solution to household level energy needs. However, P2PESS has yet to be fully explored in terms of development and adoption. As such, this study attempts to provide an understanding of the adoption intention on artificial intelligence (AI) in P2PESS a developing country. This study is realized by developing an extended adoption intention model analyzed through partial-least squares - structural equation modeling (PLS-SEM). Results show that attitude is the most significant predictor of AI in P2PESS adoption intention. This study also reveals that the trust dimension has the strongest effect on attitude, while attitude toward use has the strongest effect on behavioral intention. Also, this study confirms ease of use and usefulness as critical factors in adoption intention. Meanwhile, AI-anxiety is the least significant predictor in the model. Finally, this study is the first evidence of AI in P2PESS adoption intention from the perspective of household level users.