Social interaction has long been a subject of theoretical inquiry in both Computer-Mediated Communication (CMC) and Human-Computer Interaction (HCI), but seldom has it been examined through the lens of digital embodiment. As the metaverse gains traction as a platform for learning and collaboration, understanding how its affordances construct behavioral engagement demands empirical scrutiny. Thus, this study examines the effects of avatar customization and communication modality on behavioral engagement within a metaverse-based simulation. Using a 2×2 factorial design, participants were randomly assigned to avatar (customized vs. generic) and modality (voice vs. text) conditions, with engagement tracked via a stealth assessment approach across multiple sessions. Findings indicate that avatar customization facilitated broader spatial exploration, while voice-based communication elicited higher interpersonal interaction. Critically, the convergence of both factors produced a compounded effect that yielded selective interaction effects on temporal and social dimensions of engagement. This study contributes a framework of affordance convergence that informs both the theoretical modeling of digital embodiment and the practical design of immersive learning platforms. As educational experiences increasingly unfold within socio-technical systems, the challenge for both HCI and CMC is to design environments where social interaction is both mediated and dynamically co-constructed through the alignment of interactional affordances.

Introduction: As digital platforms increasingly mediate language learning, the challenge is no longer simply how to deliver content online but how to design environments that cultivate authentic multilingual practice. While multilingualism has long been linked to enhanced metalinguistic awareness and domain-general cognitive flexibility, the role of multimodal digital environments in fostering these outcomes remains underexplored.

Purpose: Grounded in sociocognitive and multimodal interactionist perspectives, this study examines how a cross-device metaverse platform can support multilingual development through spatially organized, task-based, and avatar-mediated interaction. Specifically, it investigates whether multilingual engagement in language-zoned virtual spaces improves learners' communicative performance, affective engagement, and cognitive control compared to conventional instruction.

Methodology: Using a quasi-experimental cluster-assigned pretest-posttest control group design, learners engaged in communicative scenarios across English, Filipino, and Mandarin within language-zoned virtual spaces that cued role-appropriate language use. Data were collected using performance-based role-play assessments (code-switching accuracy, communicative competence), oral fluency measures (WPM), motivation and anxiety questionnaires, and a Stroop interference task to assess cognitive flexibility.

Findings: Compared to peers in a control condition, learners in the metaverse environment demonstrated significantly greater gains in code-switching accuracy, spoken fluency, motivational engagement, and cognitive control. Specifically, experimental participants showed improved context-appropriate language selection and reduced cross-language interference when shifting between English, Filipino, and Mandarin during task-based role-play scenarios. They also produced more fluent spoken output and demonstrated stronger communicative competence ratings in completing real-world interaction tasks. In addition, learners reported higher motivational engagement and cognitive results, further revealing improvements in inhibitory control and attentional regulation. Collectively, these outcomes suggest that spatially cued multilingual interaction in the metaverse supports integrated gains in linguistic performance and executive functioning.

Originality/Value: This study provides empirical evidence that multilingual development is shaped not only by linguistic input but by how digital learning ecologies choreograph spatial, social, and multimodal cues into context-responsive language use. By operationalizing multilingual interaction through spatial language zoning, avatar-mediated tasks, and AI-supported multilingual dialogue, the study positions the metaverse as a semiotically rich pedagogical ecology that can simultaneously foster code-switching competence, oral fluency, motivational engagement, and domain-general executive control. The findings advance multimodal multilingual education theory by demonstrating how context-sensitive interaction design can generate co-emergent communicative, affective, and cognitive benefits in multilingual learners.

This study sought to understand the experiences of All-but-Dissertation (ABD) students that led them to withdraw from a Doctor of Information Technology (DIT) programme. A total of 27 students from three Philippine universities were interviewed using a semi-structured format. Results show that most participants were driven by extrinsic motivations and viewed graduate education as a pathway to a better life. The challenges they faced were both internal and external in nature (e.g. study-work conflicts and personal problems), which are comparable to those in other disciplines. Most reasons (e.g. limited research experience and dissertation anxiety) for dropping out from this professional doctorate align with findings from studies on Doctor of Philosophy (PhD) programmes. However, two reasons unique to ABD students in the DIT programme were the inclusion of software development and the selection of computing research topics. Overall, these findings provide empirical evidence for addressing issues related to dissertation delays, prolonged doctoral completion times, and attrition in graduate education. Practical and managerial implications derived from this study could inform graduate school policies and practices, with potential applications across other doctoral disciplines.

The metaverse is reshaping interaction, learning, and community-building in immersive virtual environments. While interest in metaverse adoption is growing, most research has focused on technological predictors and has overlooked the experiential dimensions that are central to sustained engagement in these spaces. This gap limits understanding of how users develop and maintain meaningful virtual existence in the metaverse. Therefore, this study develops the Metaverse Experience and Technology Acceptance (META) model by integrating the principles of the Technology Acceptance Model (TAM) and Embodied Social Presence Theory (ESPT). Structural equation modeling (SEM) was used to analyze data collected from 924 students with metaverse experience. The META model demonstrates strong explanatory power in accounting for both technology acceptance and user experience in virtual worlds. Moreover, the findings indicate that adoption of the metaverse as a digital university extends beyond the functional focus of TAM to include the immersive, social, and embodied elements emphasized in ESPT. By bridging technological and experiential determinants, the META model advances theoretical understanding and offers actionable insights for creating metaverse environments that promote conducive digital existence.

This study examines the Canvas’ usability in Learning Management System (LMS) from the perspectives of students and teachers, focusing on experiences across Progressive Web App (PWA) and native mobile platforms. A task-based usability testing approach was employed, combining quantitative measures of task completion and time with qualitative insights from observations and participant feedback. Findings indicate that both platforms supported high task completion, though clear differences emerged in efficiency and feature accessibility. Teachers achieved a 91.7% completion rate on the mobile app compared to 100% on the PWA. The mobile app was faster for grading and assignment creation, while the PWA provided broader feature coverage, particularly for analytics, though some users reported navigation difficulties. For students, performance differences were more pronounced: average task completion time on the PWA was 1.24 minutes compared to 5.72 minutes on the mobile app. Tasks such as replying to announcements and checking grades were completed up to ten times faster on the PWA. Overall, the mobile app demonstrated greater stability and efficiency for routine functions, whereas the PWA offered extended functionality and cross-platform access but with tradeoffs in responsiveness and interface clarity. These results highlight the role of platform choice in shaping user experience and suggest directions for optimizing Canvas LMS for both teaching and learning contexts. By advancing usability in digital learning platforms, this research contributes to Sustainable Development Goal (SDG) 4: Quality Education, while also supporting SDG 9: Industry, Innovation, and Infrastructure through insights on mobile technology design, and SDG 10: Reduced Inequalities by emphasizing accessibility across diverse devices and connectivity conditions.

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.