Volume 3, Issue 2, 2026

ABSTRACT: This study explores how the spectacle of social media and online games shapes children's lived experiences of school life and interpersonal relationships, particularly within the Indonesian cultural framework of Tri Sentra Pendidikan—family, school, and society. Anchored in a qualitative phenomenological approach grounded in Husserlian tradition, the research aims to uncover how digital behaviours are embedded in systems of imitation, symbolic power, and meaning-making. Data were collected through in-depth interviews with 21 adult informants (nine parents, nine teachers, and three principals) from three elementary schools in Yogyakarta, selected purposively for their daily proximity to children. Using epoché, reduction, imaginative variation, and synthesis, the analysis reveals that digital platforms are often perceived by children as more trustworthy than familial or school authorities. Findings highlight three key dynamics: (1) the normalisation of aggression and spectacle in digital spaces, (2) the displacement of educational authority by algorithmic content, and (3) children's shifting moral orientation shaped more by screen-based narratives than by institutional guidance. Rather than offering definitive solutions, this paper foregrounds the voices around the child—listening closely to their meanings of safety, humour, and resistance—and situates them within broader discourses of power, identity, and character education in a spectacle-saturated world.

ABSTRACT: The extent to which lecturers’ age, gender, marital status, educational qualification, and academic rank are associated with stress management among university lecturers in Nigeria was examined in this study using a multivariate approach. A cross-sectional survey design was employed, with a multistage stratified sample of 10,350 lecturers drawn from federal, state, and private universities. Data were collected using a structured questionnaire based on established measures, including selected subscales from the Brief COPE, the Multidimensional Scale of Perceived Social Support, and recovery and appraisal measures adapted from established frameworks. Multivariate analysis of variance (MANOVA) and Tukey’s HSD post-hoc tests were conducted, with partial η² reported as effect sizes (p < .05). Statistically significant multivariate associations were observed for all variables with the combined stress management outcomes (p < .001). Academic rank and age had the largest effects (ηP2 ≈ .099–.102), followed by educational qualification (ηP2  = .086), while gender (ηP2  = .060) and marital status (ηP2  = .048) had smaller effects. Higher mean scores across the stress management dimensions were observed among younger lecturers. Male lecturers had slightly higher mean scores in cognitive appraisal and self-recovery, although gender differences were generally small. Higher mean scores in social support, adaptive coping, and self-recovery were observed among divorced lecturers, noting the small size of this subgroup. Lecturers with doctoral qualifications had higher mean scores in stress awareness and adaptive coping, while senior academics had higher mean scores in social support and cognitive appraisal. Pairwise group differences were identified across several dimensions, although most effects were small to moderate in magnitude. The pattern of results indicates that variation in stress management is more strongly associated with career stage and position within the academic system than with personal characteristics alone. Implications point to the need for strengthened institutional support, particularly for early-career and lower-ranked staff, alongside consideration of group differences in the design of interventions aimed at improving well-being and productivity.

ABSTRACT: The Higher Education sector is being revolutionized by AI, with machine learning, natural language processing and generative AI applications. Yet, with the fast pace of technological evolution, both the possibilities and threats of AI incorporation have been spread out. This study includes an integrated multimethod systematic review and bibliometric analysis of the dual landscape of AI in higher education. Following PRISMA 2020 guidelines, systematic searches of Web of Science (n=1,631), PubMed (n=1,349), and Scopus (n=1,439) yielded 4,419 records (2021-2025). Following screening for duplication and eligibility, 110 studies (85 high and 25 medium quality) were included for thematic synthesis. A concurrent bibliometric analysis of 1,227 documents in Scopus was performed to depict author citation networks, keyword co-occurrence, and bibliographic coupling by source and country, using VOSviewer. The study identified Jiao, Ouyang, and Zheng as the most cited authors; the most frequently used keywords are 'ChatGPT' (173 times) since late 2022. Academic support, automated grading, teacher development, perceived usefulness, personalization of learning, and prediction of performance. The perceived ease of use, 24/7 access to technology, administrative efficiency; and academic integrity, data issues, integration barriers, technological limitations, equity concerns. And attitudinal barriers were identified as opportunity and challenge domains, respectively, during the thematic synthesis process. This study offers a dual landscape framework which means that there are no opportunities without challenges, and no challenges without opportunities. It delivers an evidence-based typology for institutional AI strategy, and priority areas for policy intervention, practically. The methodologically, it shows the usefulness of the combination of bibliometric and systematic review for complete literature synthesis. This research aims to explore the opportunities and challenges in integrating AI into higher education, based on a systematic review and bibliometric analysis of relevant literature.