The decision to select a college degree program plays a fundamental role in shaping students' academic and professional futures. While much research has examined the factors affecting students' choices prior to enrollment, fewer studies have explored their perceptions and expectations after they have entered their chosen programs. This study seeks to bridge this research gap by investigating the visions and aspirations of freshmen using natural language processing techniques. The textual analysis involves text tokenization, word frequency counts, visual representation of data through word clouds, and sentiment classification. The findings suggest the demand for tailored academic support and curriculum development that align with the specific aspirations of students in different disciplines. Overall, this study contributes to the growing literature on student decision-making and expectations of degree programs, while demonstrating the value of sentiment analysis as a tool for understanding students' academic trajectories.

This volume, Mental Health Challenges in Academia: Stressors Faced by Students and Faculty, bravely confronts the issues that many in higher education endure but few openly discuss. From the strains of balancing teaching, research, and administrative duties to the financial pressures, cultural challenges, and emotional burdens faced by students, it brings together a diverse range of perspectives to paint a holistic picture of academic life. It highlights both the systemic issues and the deep personal stories that reveal how intertwined our professional achievements are with our personal well- being. In doing so, it not only informs but also reassures its readers: you are not alone, and there are ways forward.

This chapter establishes the core foundations of upskilling and reskilling in an AI-driven workplace and discusses their importance in workforce innovation. Ongoing education and continuous learning are highlighted as key factors in making workers responsive to changing industry needs. Through the integration of industry and educational institution case study insights, such as Siemens lifelong learning initiatives, the Center for Integrated STEM Education–Massachusetts Institute of Technology Responsible AI for Social Empowerment and Education (CISTEM-MIT RAISE) AI literacy initiatives, and the Miriam College Technology Business Incubator (MC-TBI), the discussion emphasizes how workforce strategies powered by AI improve skills development, increase productivity, and establish sustainable career paths. Predictive workforce analytics, adaptive learning pathways, and industry-specific training methodologies are examined as high-impact interventions for mitigating skill deficiencies. The transforming character of learning institutions and training schemes is also considered, with special emphasis placed on the necessity for academia–industry–government partnerships for the creation of scalable and accessible learning ecosystems. The findings reveal that AI-supported reskilling initiatives, backed by established assessment methodologies and policy frameworks, significantly improve workforce adaptability and long-term employment prospects, promoting ongoing learning environments, accessibility-focused training solutions, and collaborative partnerships to future-proof workforce in the digital economy.

This book offers a comprehensive guide to navigating the transformation of the workforce due to the influence of artificial intelligence (AI) across industries and discusses detailed strategies for executing reskilling and upskilling programs for professionals and managers in charge of workforce development, training, and employee retention in an AI-driven landscape. As AI continues to reshape sectors and redefine job roles, the need for a strategy for an adaptable and well-equipped workforce has never been more critical. By analyzing AI’s integration with other emerging technologies, such as blockchain and IoT, and their specific impact across sectors, this book prepares readers to meet the unique demands of an AI-driven workforce transformation.

We owe this amazing collection to the vision and dedication of Aras Bozkurt of the Anadolu University (Türkiye), who, with characteristic energy and imagination, has brought together a remarkable community of voices. Having collaborated with Aras on several AI in education research, I can say with certainty that he not only advances the field with rigor but also infuses it with creativity and warmth. This volume reflects that spirit: diverse in scope, rich in insight, and committed to the future of education as a human endeavor. To Aras and to all the book chapter con tributors, not only my thanks but the thanks of every reader who will learn, reflect, and dream alongside these pages. Your work is a gift to our field and a reminder that scholarship can be both courageous in its critique and generous in its vision. Rarely does a book succeed in being both a mirror to our present and a lantern lighting the path ahead.

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.

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.