Exploring the Impact of AI Tool Usage and Course Design on Student Satisfaction in Asynchronous Environments

The increasing adoption of asynchronous learning has brought both opportunities and challenges in ensuring student satisfaction. This study investigates the influence of AI tool usage and learning design-along with supporting variables such as student attitude, engagement, cohesiveness, course design and affective commitment-on student satisfaction in asynchronous learning environments. Using a quantitative approach, data were collected in April to May 2025 from 512 respondents primarily composed of students in Greater Jakarta area (Cities of Jakarta, Bogor, Tangerang, Depok, and Bekasi in Indonesia). Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to evaluate the research model. The findings reveal that AI tool usage, course design, attitude, affective commitment, and student engagement significantly affect student satisfaction, while student cohesiveness does not show a significant impact. This research highlights the importance of integrating technological tools and thoughtful course design in asynchronous education, offering practical insights for educators, instructional designers, and education policymakers to enhance learning experiences in flexible online settings.