Predicting Generative AI–Based Learning Among Students: The Roles of Adaptive Learning Motivation, Technology Openness, and Digital Collaboration Readiness - Ridwan Daud Mahande (Information Technology Education Journal, 2025)
The integration of Generative Artificial Intelligence (AI) in higher education presents opportunities and challenges related to student readiness as the main users of technology. This study aimed to analyze the role of adaptive learning motivation, technology openness, and digital collaboration readiness in predicting student perceptions of Generative AI-based learning. A quantitative approach with an explanatory design was used through a survey of 370 students from the Faculty of Engineering, State University of Makassar, Indonesia. The data were analyzed using Partial Least Squares structural equation modeling (PLS-SEM). The results showed that the three predictor variables had a positive and significant effect on students' perception of Generative AI-based learning, with adaptive learning motivation being the most dominant factor. In addition, a pattern of tiered relationships was found, in which adaptive learning motivation affects openness to technology, which further strengthens the readiness for digital collaboration. The research model explained 60.8% of students' perceptions of AI-based learning. These findings confirm that the success of Generative AI integration is not only determined by technological readiness but also by students' psychological and digital readiness. This study contributes to expanding the model of learner readiness in the AI-based education ecosystem.