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.

Soil pollution in urban environments is a critical issue with significant implications for public health, environmental quality, and sustainable urban development. This review paper explores the various aspects of soil pollution in urban areas, including its sources, types of pollutants, consequences, mitigation strategies, and the importance of sustainable urban development. The sources of soil pollution in urban environments are diverse, ranging from industrial activities and waste disposal to emerging sources like microplastics and pharmaceuticals. The types of pollutants found in urban soils include heavy metals, polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides, among others. The consequences of soil pollution in urban areas encompass public health risks, environmental impacts, and contamination of water resources. Mitigation strategies such as remediation techniques, pollution prevention measures, urban green infrastructure, and public awareness are crucial for addressing soil pollution in urban environments. Sustainable urban development plays a key role in maintaining soil health and integrating soil considerations into urban planning. By understanding the sources, types, consequences, and mitigation strategies of soil pollution in urban areas, cities can work towards healthier environments and sustainable development.

The study investigated the live selling of apparel on Facebook as a form of entertainment that blends digital marketing with e-commerce. It examined how satisfaction mediates the effect of the factors associated with apparel live selling on Facebook on Gen Z’s repurchase intention. A descriptive-casual method utilizing the Partial Least Square – Structural Equation Modelling (PLS-SEM) analyzed the results and tested the hypotheses from the data gathered from 371 respondents. Results showed that satisfaction partially moderates the causal relationship between the exogenous variables and repurchase intention. Researchers found that live sellers can create more value for customers and better serve them by focusing on important factors like product range, delivery speed, and customer service. The indirect effect of the factors influencing live selling on repurchase intention through customer satisfaction was significant. This means that part of the causal relationship between the factors of Live Selling and Gen Z's repurchase intention towards Facebook Live Selling was mediated by Satisfaction. The major contribution of this study is the identification of the six key factors that influence Gen Z customers' likelihood to repurchase an apparel brand found on Facebook through live selling. By highlighting the mediating role of customer satisfaction, this study reveals how retailers can increase loyalty and repeat purchases by focusing on providing customers with a positive shopping experience. Moreover, the study provides valuable insights into what makes Gen Z different and how marketers can tailor their products to appeal specifically to this important consumer group.

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.

This study addresses the critical importance of customer-focused strategies and brand promotion within the food component import–export sector, a context that remains underexplored, particularly with respect to the influence of external factors across diverse geographical settings. Its objective was to empirically investigate how operational excellence, particularly customer relationship management (CRM), value-added services, and product quality and safety assurance affects customer loyalty and, in turn, drives brand promotion. Utilizing a descriptive quantitative research design, a cross-sectional survey was administered in the Philippines, with data analyzed through Structural Equation Modeling (SEM). The findings indicate that CRM exerts the most significant influence on customer loyalty, while product quality and safety assurance also positively contributes. Importantly, customer loyalty emerged as a strong driver of brand promotion. The model demonstrated high predictive power for customer loyalty and substantial explanatory power for brand promotion. These results highlight that strong customer relationships developed through CRM are essential for fostering loyalty and positioning customers as effective brand advocates. Preserving product quality and safety assurance is fundamental, the study's results imply that companies should purposefully put in comprehensive CRM systems and tailored encounters. To develop devoted clients, hence boosting brand awareness and securing sustained competitive advantage. Future studies ought to broaden the scope of investigated factors and explore more generalizable geographical settings to improve generalizability.

This study examines how multimedia arts students perceive generative artificial intelligence (AI) as a potential threat to traditional artistic practices. While AI-powered tools are increasingly integrated into creative processes, offering benefits such as efficiency, expanded techniques, and access to new artistic possibilities, their use has sparked concerns about the declining role of manual skills and the authenticity of AI-generated works. Using Rogers’ Diffusion of Innovations Theory as a framework, this research explores student insights through qualitative analysis, including keyword frequency and word cloud visualization. Results indicate a generally positive attitude toward AI’s practical applications, yet students express reservations about its impact on creative identity, originality, and ethical considerations such as copyright and attribution. These findings suggest a need for educational approaches that introduce AI critically and constructively, ensuring that emerging technologies enhance rather than replace human creativity. Future research should explore pedagogical strategies that preserve traditional artistic values while preparing students to navigate an evolving creative landscape.

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.

Digital portfolios have become an essential assessment tool in project-based and student-centered learning environments. Unfortunately, students exert minimal effort in creating digital portfolios because they find the writing component unchallenging. This issue is concerning since existing research predominantly focuses on the use of pre-existing platforms for building digital portfolios. With this concern, there is an opportunity to explore more challenging approaches to digital portfolio creation. Consequently, this study employs a project-based learning (PBL) approach within a website design and development course, where 176 undergraduate students completed weekly coding tasks culminating in a self-coded digital portfolio. Using a one-group posttest-only research design, data were collected through a structured questionnaire that included demographic items and validated scales measuring learning effectiveness and ownership of learning. The survey was administered electronically after students submitted their digital portfolio projects. The results reveal that device ownership shows only weak associations with students’ perceptions, while internet connectivity and self-reported academic performance demonstrate stronger relationships with engagement and ownership of learning. Additionally, prior experience with digital portfolios positively influences students’ engagement, motivation, and ownership of learning. Implications of these findings are discussed for supporting the integration of digital portfolios into technical disciplines. Overall, this study contributes to the literature on PBL methodology, expands our understanding of digital portfolio integration, and underscores the significance of student-centered pedagogies.

This study seeks to predict the intention to adopt greener last-mile delivery in telemedicine supply chain using machine learning-based approach. Data used in the study were acquired from 349 respondents in the Philippines, and examined using different machine learning techniques, namely, Gradient Booting, Random Forests, Support Vector Machines, K-Nearest Neighbor, XGBoost, and Decision Tree further validated using various performance metrics. Results demonstrated that more than 80% of machine learning models' performance accurately predict intention to adopt greener last-mile delivery in telemedicine. Moreover, RF, SVM, and XGB attained optimal prediction performance. Attitude towards greener delivery, perceived behavioral control, perceived usefulness, were the most significant factors influencing the intention to adopt greener last-mile delivery, followed by subjective norms and trust in technology. Interestingly, perceived ease of use ranks the lowest, indicating that intention to adopt greener last-mile delivery among individuals is mostly affected by attitude to support green logistics and their perceived benefits rather than the ease and trust of using this technology. Lastly, theoretical and practical implications, together with effects in telemedicine supply chain are presented at the end of the study.