Keyword: learning engagement

ABSTRACT: This study proposes an integrated, evidence-based model to explain the transition from artificial intelligence (AI)-related anxiety to acceptance and learning engagement in the context of Education 4.0. Drawing on a secondary data synthesis of four nationally representative South Korean datasets—KISTEP (2024), NIA (2024), KEEP (2024), and Statistics Korea (2024)—and grounded in Keller’s (1987) ARCS motivational model and Davis’s (1989) Technology Acceptance Model (TAM), the study develops a multi-layered framework linking AI anxiety, AI literacy, AI acceptance, and learning engagement. The analysis tests four hypotheses using a pseudo-SEM approach. Because the study relies on secondary data, proxy indicators are employed to operationalize key constructs, with the Digital Competency Index (DCI) used as a proxy for AI literacy. The findings show that AI anxiety is significantly and negatively associated with AI acceptance, whereas AI literacy exerts a direct positive effect and also weakens the anxiety-acceptance relationship, functioning as a cognitive buffer (moderating effect, β = .23, p < .05). In turn, AI acceptance emerges as a strong predictor of learning engagement (R² = .34). Most notably, this study reframes AI literacy not merely as a technical skill, but as a resilience mechanism that helps reduce technology-related anxiety. The theoretical contribution has direct implications for instructional design and age-sensitive policy interventions. In addition, the pseudo-SEM approach offers a replicable framework for building macro-level theory from national administrative data.

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.