Keyword: artificial intelligence

ABSTRACT: Artificial intelligence (AI) is rapidly reshaping school leadership within Education 4.0, offering enhanced decision-making and organisational efficiency while intensifying ethical concerns regarding transparency, bias, and accountability. Existing research has largely treated these opportunities and risks as separate phenomena, overlooking the relational processes through which AI is enacted in practice. This paper advances a process-based conceptualisation by positioning trust as the central mediating mechanism in AI-enabled school leadership. It argues that AI does not produce outcomes directly; rather, its effects are contingent on how it is accepted, interpreted, and enacted within school contexts. The proposed framework shows that trust shapes whether AI leads to constructive outcomes, including ethical use, professional engagement, and improvement, or to disruptive consequences such as resistance and mistrust. Leadership is conceptualised as a key antecedent of trust, highlighting the centrality of relational governance in the effective and responsible integration of AI in schools.

ABSTRACT: This study explores the behavior patterns of the Arab Generation Z in relation to consumption of artificial intelligence (AI) assisted media content on digital platforms based on the Uses and Gratifications Theory (UGT) and the Interactive Theory. With the advent of AI personalization and curation of the digital experience, it is now more than ever that the way the young audience perceives and engages with the algorithmically generated content is being questioned. The research uses a mixed-methods design whereby quantitative and qualitative methods are integrated to achieve the reliability of statistics and interpretations. The quantitative stage consisted of 428 Arab Gen Z surveyed on motivations, interactive behaviors and perceived gratifications using a five-point Likert scale and applied on an online questionnaire. To examine the perceptions, attitudes, and awareness of AI as a force of influencing digital content, the qualitative phase involved ten semi-structured interviews to understand the perspective of the participants. Also, the use of a content analysis of 25 posts about AI-assisted social media on the major platforms (Tik Tok, Instagram, and YouTube) was performed to find the visual or textual signs of AI integration, including synthetic voices or auto-generated captions. The results showed that 68 percent of the respondents were strongly interested in AI-suggested content and 72 percent said they received powerful informational and entertainment satisfaction. Nevertheless, the awareness of AI presence in media production manifested itself in only 61 percent. Qualitative data implied ambivalent views the fascination with the creative possibilities of AI combined with the fear of authenticity and manipulation of emotions. Its results confirm a Hybrid Model of Gratification, where intrinsic motivations (e.g., entertainment and self-expression) are interacting with algorithmic affordances (e.g., AI personalization), and it has a significant predictive value of engagement behaviors (r = .61, p <.001). Engagement (r = .46, p < .001) and authenticity attitudes (r =.38, p <.01) were moderately related to the AI awareness. This model is the major theoretical contribution of the study to the expansion of UGT in AI-mediated situations.

ABSTRACT: Artificial intelligence (AI) has increasingly played an important role in educational development in the digital era. Teachers, therefore, need appropriate knowledge and perceptions regarding the use of such technology in instructional practices. This study aimed to: 1) develop and examine the construct of a scale measuring Thai teachers’ perceptions of the use of artificial intelligence in instructional management, and 2) investigate the level of Thai teachers’ perceptions regarding the use of artificial intelligence in teaching and learning. The research was conducted in two phases. Phase 1 involved instrument validation using Exploratory Factor Analysis with a sample of 353 teachers. Phase 2 examined teachers’ perceptions of the use of artificial intelligence in instructional management with a sample of 298 teachers. Data were analyzed using exploratory factor analysis and descriptive statistics. The findings revealed that teachers’ perceptions of AI in instructional management consisted of several components reflecting different dimensions of technology integration in teaching. Overall, Thai teachers demonstrated a moderate to high level of perception regarding the use of artificial intelligence in instructional practices. The results provide useful implications for promoting the effective integration of artificial intelligence in educational settings.

ABSTRACT: Students today learn mathematics in a world full of digital tools and instant access to information, yet many still find the subject difficult and overwhelming. This situation raises important questions about how learning is affected when technology becomes both a support and a source of confusion. The study used a structured, quantitative approach to examine how students experience mathematics in a digital learning environment, drawing on responses from first-year college students collected through a validated questionnaire. The study found that students showed strong engagement with technological and AI-based tools. However, their mathematical competence was weakened by high anxiety, low motivation, and limited confidence. Significant differences across eight dimensions revealed that emotional, environmental, and identity-related factors were the most vulnerable areas, compared to cognitive and technological strengths. These results show that improving mathematical readiness requires not only access to digital resources but also stronger support for students’ emotional well-being and learning environments.

ABSTRACT: This study highlights that understanding how faculty adopt technology requires integrated theoretical frameworks rather than the single-theory models often seen in current research. Faculty responses to disruptive technologies, such as generative AI (GenAI), involve complex psychological processes that are frequently overlooked by traditional models. To address this, we developed the Mediated Message Model (MMM) by combining communication theory, behavioral prediction, and motivational psychology, targeting four gaps: fragmented focus, lack of contextual sensitivity, limited process understanding, and constraints. We utilized this framework to design and evaluate a faculty development program featuring a book club format, involving fifty-six faculty members across two cohorts during the 2024–2025 academic year. Data from surveys (n = 30), interviews (n = 6), and action plans (n = 28) supported our predictions, demonstrating that faculty responses depend on interactions between perceived efficacy and value, rather than solely on individual psychological factors. Our analysis identified four distinct cognitive-behavioral outcomes—engaged adoption, impassive acceptance, discouraged hesitation, and aversive rejection—that stem from specific efficacy-value combinations. Faculty members needed multiple stimuli—such as personal experiences, peer demonstrations, and authoritative readings—to effectively adopt GenAI, as no single approach was sufficient. The study also revealed goal orientation patterns indicating that intrinsic versus extrinsic motivation influences technology integration, opening avenues for future research. The MMM advances both theory and practice by aiding faculty development leaders in designing comprehensive, evidence-based strategies that consider the psychological complexity involved in the adoption of GenAI.