Acceso usuarios
Arabia Saudí
The research explores the role of interest in mobile learning environment on students’ satisfaction with AI tools, while visual learning styles as a mediator and learner-instructor interaction and teacher-generated responsibility climate as moderators. The study helps fill critical gaps in understanding the influences of mobile learning environments and pedagogical dynamics on AI tool satisfaction among university students. This research adopted a quantitative approach and made use of PLS-SEM to analyze the data collected from 309 students from various courses in universities. Validated scales were used for measuring mobile learning interest, visual learning style, learner-instructor interaction, responsibility climate, and satisfaction with AI tools. An online survey was conducted to collect the data that was analyzed in order to evaluate direct and indirect relationships. The findings reveal that mobile learning environment interest significantly enhances students’ satisfaction with AI tools. Visual learning style mediates this relationship, emphasizing the role of personalized learning preferences. Furthermore, learner-instructor interaction and responsibility climate positively moderate the relationship, highlighting the importance of pedagogical and environmental factors in fostering student satisfaction with AI technologies. This study contributes to existing literature by integrating pedagogical and technological constructs in the context of mobile learning environments. It offers practical insights for educators to design engaging, student-centered learning environments that optimize satisfaction with AI tools.
