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Artificial Intelligence (AI) has a profound impact on university teaching and learning. However, there is a lack of instruments for measuring university students’ motivation to use AI in their learning. In this study, we developed and validated a questionnaire to measure students’ motivation to learn with AI. In Study 1, we developed the AI Motivation Scale (AIMS). Rooted in self-determination theory, the scale measures university students’ motivation to learn with AI across five dimensions: intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. Both within-network and between-network validation analyses indicated that the AIMS is psychometrically sound. In Study 2, we used the AIMS to explore whether students’ motivation to learn with AI is influenced by their university environment and promotes their engagement in learning with AI. The results showed that motivation to learn with AI mediated the positive relationship between supportive environments and engagement in learning with AI. The study shows that AIMS is a psychometrically sound instrument that can be used to assess university students’ motivation to learn with AI. It also sheds light on the pivotal role of motivation to learn with AI in the higher education context.
Li et al. (Mon,) studied this question.