Keyword: age

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: The integration of digital technologies has enhanced language learning by improving access to resources, interaction, and learner autonomy in Rwanda. National Information and Communication Technology initiatives support competence-based education, yet the use of digital tools in classrooms remains uneven. Despite these efforts, many students are not fully engaged or motivated when using digital language learning tools. This study therefore sought to examine students’ perceptions of digital language learning and their influence on academic engagement and motivation in Rwandan secondary schools. A quantitative approach using a cross-sectional explanatory design was adopted. Data were collected from 200 secondary school students in Kamonyi District through a structured questionnaire based on a five-point Likert scale. Descriptive and inferential statistics, including correlation, regression analysis, and Structural Equation Modelling (SEM), were used to analyze relationships among perception, engagement, and motivation. The results revealed that students have highly positive perceptions of digital language learning tools, particularly in enhancing understanding, confidence, and independent learning. Significant positive relationships were found between perception and engagement (r up to 0.66) and between perception and motivation (r = 0.64). Regression analysis showed that perception (β = 0.49) and engagement (β = 0.37) significantly predict motivation, explaining 54% of its variance. SEM findings further confirmed that engagement partially mediates the relationship between perception and motivation. The study concludes that positive student perceptions significantly enhance engagement and motivation in digital language learning. It implies that improving students’ experiences with digital tools is essential for better learning outcomes. The study recommends increased investment in digital infrastructure, enhanced teacher training, and the integration of interactive, learner-centered digital strategies to optimize language learning in Rwandan secondary schools. 

ABSTRACT: This study examined the relationship between AI-powered learning tools, student engagement, and academic performance in higher education, with a focus on differences across academic disciplines, age groups, and gender. The study employed a quantitative, correlational, and causal-comparative research design, involving undergraduate students from both STEM and non-STEM disciplines through a multi-stage sampling approach. Data were obtained from AI-generated learning metrics, specifically Time-on-Task, Interaction Frequency, and Knowledge Mastery, alongside a structured questionnaire measuring behavioral, cognitive, and emotional aspects of student engagement, as well as students’ self-reported academic performance. The findings revealed that student engagement varied according to the type of AI learning tool utilized. Tools designed to support knowledge mastery were associated with higher levels of engagement compared to those focused primarily on interaction frequency or time spent on tasks. Students in STEM-related disciplines generally demonstrated stronger engagement than those in non-STEM fields, although the pattern of association between AI tool use and engagement was consistent across disciplines. Knowledge Mastery also emerged as the most influential factor in predicting academic performance across different age groups, with older students tending to achieve better academic outcomes. Additionally, gender differences were observed in how students benefited from specific AI tools, suggesting varying learning preferences and responses to AI-supported instruction. Overall, the study highlights the significant role of AI-powered learning tools in shaping student engagement and academic performance. It emphasizes the need for mastery-oriented, learner-sensitive, and discipline-responsive AI interventions to optimize learning outcomes in higher education.

ABSTRACT: Teacher burnout has become a critical issue in education, threatening the sustainability of schools by diminishing the well-being and effectiveness of educators. This qualitative study explores the causes, consequences, and potential interventions for teacher burnout through in-depth discussions with eleven participants. The study draws on thematic analysis to uncover key stressors, including excessive workload, lack of administrative support, and systemic pressures. Our findings highlight the impact of burnout on classroom management, teacher-student relationships, and institutional cohesion. The discussion underscores the importance of strategic planning by school districts, emphasizing the need for well-paced, teacher-friendly programming to support educators. The study concludes that reducing administrative burdens and fostering a supportive work environment is essential to mitigating burnout and ensuring a sustainable education system.

ABSTRACT: In Vietnamese public schools serving ethnic minority learners, English language instruction often unfolds in linguistically complex and culturally mismatched classrooms. This narrative case study explores how one Vietnamese English as a foreign language (EFL) teacher responds to the challenges of teaching English to Khmer learners in a rural secondary school in the Mekong Delta. Drawing on a written reflection composed during a professional development course, the study examines how the teacher makes sense of her learners’ persistent grammatical difficulties, rooted in first language (L1) transfer from Khmer, and how she navigates the cultural dissonance between textbook content and learners’ lived experiences. Findings reveal that the teacher reframes language “errors” as patterned responses to structural distance, and that she enacts responsive teaching through chunk-based instruction, visual scaffolding, and culturally localized tasks. Her practice illustrates how small, context-driven adaptations are reconfigured when English is learned through Vietnamese by Khmer-speaking learners, making visible the interpretive work teachers do to turn structural distance into pedagogical resource. By foregrounding the voice of a teacher working in a triadic language environment (Khmer, Vietnamese, and English), the study theorises teacher responsiveness in such settings and offers an empirically grounded account of multilingual pedagogy and teacher agency in under-researched Southeast Asian classrooms.

ABSTRACT: Kazakhstan’s trilingual education policy and ongoing educational reforms have created unique challenges for teachers, yet systematic research on educator job satisfaction remains limited in post-Soviet contexts. This study addresses critical gaps by examining how Kazakhstan’s specific educational landscape – including language policy disparities, reform pressures, and cultural transitions – influences teacher job satisfaction across demographic and professional variables. Using the Teacher Job Satisfaction Questionnaire (TJSQ) with 383 teachers nationwide, we employed Herzberg’s Two-Factor Theory and the Job Demands-Resources Model to analyze satisfaction patterns across grade levels, teaching subjects, academic qualifications, marital status, and teaching language. Results reveal significant disparities: Kazakh-medium teachers report lower responsibility satisfaction than Russian/English-medium teachers (p < 0.001), STEM teachers show higher security and recognition satisfaction than non-STEM teachers (p < 0.001), and unmarried teachers demonstrate greater job satisfaction across multiple dimensions. These findings illuminate how Kazakhstan’s unique socio-cultural and linguistic context mediates traditional job satisfaction factors, necessitating culturally adapted policy interventions. Recommendations include targeted resource equity for Kazakh-medium instruction, differentiated support for primary teachers, and recognition programs addressing cultural values of collective responsibility.