Vol 6 No 2 (2026)

In rural low-resource physical education settings, whether an offline-priority embodied AI feedback protocol is feasible and can improve students' task-specific expression of bodily sensations still requires empirical testing. This quasi-experimental study involved 72 first-year rural high school students (36 males and 36 females) randomly assigned to either the embodied AI group or the traditional AI feedback group. Both groups completed a 3-week standing long jump intervention (one 45--60 minute class per week, with 8 attempts per class). The experimental group used an offline-priority mobile learning ecosystem---teacher smartphone rapid key frame capture, data cable offline upload, local server AI processing, and iFlyTek voice input---to guide the "awareness--prediction--re-practice'' loop, while the control group received only traditional technical feedback. The teacher circulated between the two groups during instruction. Linear mixed-effects models and Bootstrap mediation analysis were employed to examine the effects. Results showed that standing long jump distance improved by approximately 13% in both groups (embodied AI group: +13.13%; traditional AI group: +13.11%), with a non-significant time × group interaction (p > 0.05). However, the embodied AI group demonstrated significantly higher task-specific body-awareness expression scores than the control group (Cohen's d = 3.239, p < 0.001). Mediation analysis indicated that task-specific body-awareness expression did not significantly mediate the relationship between group assignment and improvement in jump performance. The findings suggest that an offline-priority embodied AI feedback protocol is feasible in rural low-resource environments and effectively enhances students' ability to describe, monitor, and predict bodily sensations in the specific context of standing long jump practice. However, it did not produce superior short-term gains in standing long jump performance compared with traditional AI feedback. Because the embodied protocol necessarily involved more dialogue turns and reflection time, future studies should employ yoked designs to isolate the independent effects of feedback type and interaction dosage.

Generative artificial intelligence (AI) is changing teaching, learning, evaluation, and feedback methods in educational settings. An increasing body of research indicates that while AI-supported technologies improve efficiency, customisation, and access to learning resources, they may also reduce evidence of meaningful learning while simultaneously improving observable academic performance. Using interrelated pedagogical, cognitive, ethical, assessment, and policy viewpoints, this study critically investigates AI in education. The study provides an integrative understanding of AI integration in education by synthesizing literature from empirical investigations (n = 21), conceptual papers (n = 19), policy reports (n = 5), and one review study using a critical narrative review design. The results show that although AI improves feedback, student engagement, instructional support, and chances for adaptive learning, it also creates issues with cognitive dependence, academic integrity, algorithmic bias, data privacy, inequality, and assessment validity. The review's main conclusion is that there is a growing conflict between meaningful learning and visible academic achievement since AI-mediated outputs might not always demonstrate prolonged cognitive engagement or conceptual grasp. The study contends that pedagogical design, teacher involvement, AI literacy, institutional governance, and technological competence all play a role in the educational usefulness of AI. The paper emphasizes the necessity of learning and assessment systems that support responsible, egalitarian, and learner-centered AI integration in educational settings while preserving learner agency and making thinking visible.

This study investigates how mobile learning supports self-directed learning (SDL) in programming education through a case study of SoloLearn. A cross-sectional survey of 708 undergraduate students at the University of Education, Winneba examined app usage, perceptions, and programming confidence, framed by Knowles’ (1975) SDL Theory and Fredricks et al.’s (2004) Student Engagement Framework. Partial least squares structural equation modeling (PLS-SEM) revealed a sequential pathway from self-management to motivation, monitoring, strategy use, and academic performance. Most participants (46.8%) reported increased confidence, while 38.4% reported improvements in real-world application skills, though challenges included limited advanced content, ad disruptions, and insufficient feedback. The findings suggest that SoloLearn effectively develops foundational SDL skills but requires adaptive features, project-based modules, and improved collaborative tools to support deeper learning. Results should be interpreted cautiously due to the single-institution, male-dominated sample. Overall, the study makes three contributions: it provides empirical evidence of a sequential SDL pathway from self-management to academic performance in mobile programming education; it demonstrates the value of combining objective grade data with self-report measures in mobile learning research; and it offers practical guidance for integrating mobile coding platforms into programming instruction across diverse educational contexts.