This study examined the role of knowledge management in predicting the educational sustainability of generative AI. A research model based on UTAUT was developed to assess the key factors associated with the educational sustainability of generative AI. A multi-analytical approach, combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN), was used to analyze data from 575 university students. The PLS-SEM results revealed that knowledge management practices, particularly knowledge acquisition and application, are significantly associated with the educational sustainability of generative AI. In addition, performance expectancy, social influence, and facilitating conditions were significant predictors of educational sustainability. The ANN findings indicated that knowledge application was the most influential predictor of educational sustainability. The study concludes that knowledge management practices, particularly knowledge application and acquisition, are meaningfully associated with students’ perceived educational sustainability of generative AI. The findings underscore the importance of developing effective knowledge management practices to integrate generative AI technologies in academic settings successfully.
This study aims to explore publication trends, key themes, collaboration networks, citation patterns, and research clustering in technology-based learning media during the period 2016–2025, with a total analyzed documents (N = 474). The analysis results indicate that themes such as artificial intelligence (AI), machine learning, augmented reality (AR), and data analytics have become key focuses in this research, reflecting the evolution of technological approaches in education. Publication trends show a significant increase, particularly during the period 2020–2024, highlighting strong academic attention to this topic. This study also emphasizes the importance of cross-institutional and inter-researcher collaboration, with collaboration networks centered on several key contributors who have made significant contributions to the development of research. Clustering analysis reveals close relationships between major research themes, such as the transition from flipped classroom-based learning to digital learning media and the integration of new technologies like AR and data analytics to enhance immersive learning experiences. This research not only provides insights into current positions and future trends in technology-based learning media but also lays a foundation for further exploration of the impact of this technology on learning effectiveness in various educational contexts.
This study employed a qualitative interview-based design to explore educators’ experiences integrating AI tools into English language teaching at a public university in northern China. Purposive sampling was employed, and 25 participants were interviewed. The data was interpreted using transactional theory, focusing on the appraisal, emotional and coping factors educators experienced when using AI in teaching. Findings showed that AI tools have significantly enhanced language skills such as grammar, speaking, listening, writing, and vocabulary while improving student engagement through gamification, personalized learning, and real-time feedback. Additionally, the study found that institutional support and professional development were crucial in mitigating educators’ stress and anxiety related to AI integration. While AI boosted positive emotions such as increased self-esteem and reduced classroom management stress, negative emotions like anxiety and unease were tied to inadequate training and rapid technological changes. The study emphasizes the need for continuous professional development and institutional support to maximize the benefits of AI in education. Based on the findings, the study suggests a pedagogical framework that conceptualises AI integration as a transactional and emotionally responsive process. The framework is significant in AI education research by foregrounding emotions and coping strategies as central mechanisms in sustainable, human-centred pedagogical transformation in English language learning.
This article presents a reflective practitioner framework for integrating artificial intelligence (AI)-enabled avatars and extended reality (XR) environments into cybersecurity education. Grounded in constructivist, experiential, and social-cognitive learning perspectives, the framework addresses the challenge of teaching applied cybersecurity concepts in online, hybrid, and diverse learning environments. Rather than reporting formal empirical findings, the article offers a practice-informed instructional model that combines scenario introduction, immersive exploration, and reflective discussion. AI avatars are positioned as tools for role-based storytelling, scenario framing, and accessible communication. At the same time, XR environments are used to support situated exploration of cybersecurity incidents such as phishing, ransomware, and data breaches. The article contributes to educational technology practice by offering design principles, implementation guidelines, and reflective considerations for instructors seeking to create more active, inclusive, and ethically grounded cybersecurity learning experiences.
This qualitative study investigates faculty perceptions of integrating artificial intelligence (AI) into higher education in Iraqi Kurdistan, a resource-constrained context, focusing on pedagogical change, cognitive impacts, and governance. Semi-structured interviews with 28-university faculty were analyzed using thematic analysis. Three core themes emerged; Pedagogical Transformation and the Reconfigured Role of Faculty, AI Student Learning Outcomes and Academic Authenticity, and Ethical Ambiguity, Social Concerns, and Regulatory Uncertainty. Participants reported enhanced personalization and administrative efficiency but expressed concerns about unreliable connectivity, academic authenticity, unequal access, institutional trust, and a critical absence of policies. Recommendations include developing context-sensitive AI policies, investing in sustained faculty professional development, designing authentic AI-resistant assessments, adopting participatory governance, and strengthening verification mechanisms. The study offers practice-focused guidance for educational leaders in Iraqi Kurdistan and similar settings seeking to leverage AI while preserving the humanistic core of higher education across teaching and learning and safeguarding equity and academic rigor.
