The data of the article "Development and validation of the artificial intelligence learning intention scale (AILIS) for university students"

As artificial intelligence (AI) permeates almost all aspects of our lives, university students need to acquire relevant knowledge, skills, and attitudes to adapt to the challenges it poses. This study involved the development and validation of a scale, called the Artificial Intelligence Learning Intention Scale (AILIS), which measured the relevant factors that shape university students’ behavioral intentions to learn about AI. We recruited 907 Chinese university students who answered the survey. The scale is comprised of 9 factors that are categorized into various dimensions pertaining to the epistemic capacity (AI basic knowledge, programming efficacy, designing AI for social good), facilitating environment (actual use of AI systems, subjective norms, access to support and technology), psychological attitudes (resilience, optimism, personal relevance), and outcomes (behavioral intention to learn AI, actual learning of AI). Reliability analyses and confirmatory factor analyses indicated that the scale has acceptable reliability and construct validity. In addition, structural equational modelling found that 16 out of the 17 hypotheses tested were supported. Overall, the findings reveal that Chinese university students express a strong intention to learn about AI, and this behavioral intention is closely associated with actual learning. We also found several key factors associated with students’ behavioral intention to learn AI. The study contextualizes the theory of planned behavior for university AI education, provides guidelines on the design of AI curriculum courses, and means of assessing course effectiveness.