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.

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.

The rise of digital platforms has led to a massive influx of textual data. While traditional textual analysis techniques have been effective, analyzing large datasets is becoming impractical due to the required time and resources. To demonstrate the usefulness of text mining as an alternative, this study analyzed data extracted from an emergency remote learning (ERL) environment. Free-form responses from a series of cross-sectional surveys (2020–2022) were analyzed using word frequency, collocation, concordance, topic modeling, and sentiment analyses. According to the findings, the most commonly occurring unigram and bigram in the text corpus were “hard” and “mental health,” respectively. Three primary themes based on lived experiences were identified, namely individual, academic, and technological challenges, and another three themes emerged from coping strategies, including entertainment, relationship, and health-related mechanisms. Negative sentiment toward the ERL setup was also evident in the text corpus. Overall, the combination of text mining techniques allowed for a comprehensive exploration of the linguistic features of the corpus and provided a multifaceted understanding of the selected phenomenon. Consequently, this study endorses text mining as a methodology for analyzing large volumes of textual data.

This chapter explores the developmental implications of artificial intelligence (AI) in contemporary educational settings. Employing a dual-methodological approach that combines interdisciplinary expertise with an integrative literature review, the chapter explores how AI technologies are reshaping student identity, emotional regulation, motivation, autonomy, and interpersonal relationships. Drawing on developmental psychology, educational theory, and empirical research, it interrogates the unintended consequences of algorithmic surveillance, pedagogical automation, and AI-mediated social interactions. The analysis highlights both the affordances and risks of AI in education, including its impact on emotional resilience, self-directed learning, and the construction of academic identity. Reframing the discourse away from purely technical efficiency toward developmental integrity makes visible the deeper human stakes of AI integration in education. The chapter consequently calls for ethical, inclusive, and human-centered approaches to AI design and implementation in schools.

The integration of digital technologies in education has profoundly transformed teacher education, necessitating a focus on creativity, problem-solving, and inquiry-based learning. Despite the expanding literature on technological self-efficacy, creativity, and curiosity in education, significant gaps persist in understanding their relationships, especially in teacher education. Utilizing a cross-sectional design, the study applies PLS-SEM to investigate the relationships among technological attitudes, technological self-efficacy, technological problem-solving engagement, intrinsic motivation, learning engagement, pedagogical knowledge, content knowledge, creative reasoning, and curiosity among 875 respondents from a state university in Cebu City, Philippines. The findings reveal that positive technological attitudes significantly enhance technological self-efficacy, which, while influencing technological problem-solving engagement, does not directly impact creative reasoning or curiosity. Additionally, both technological problem-solving engagement and intrinsic motivational factors substantially contribute to fostering creativity and curiosity. The strong roles of pedagogical knowledge and content knowledge further emphasize the need for teacher education programs to incorporate holistic strategies that combine technological engagement with pedagogical frameworks. These insights underscore the importance of equipping pre-service teachers with the skills and knowledge necessary to cultivate creativity and curiosity in their future classrooms, thereby enhancing overall educational effectiveness.

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.

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.

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.

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.

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.