Game-Based Learning for Asynchronous AI Literacy Course: Approach to Improve Students' Cognitive, Behavioural, Affective, and Ethical Learning of AI

Educators in higher education face persistent challenges in scaling AI literacy across disciplines and helping novice learners understand abstract AI concepts. Although research on game-based learning (GBL) reports mixed outcomes, few studies have examined its large-scale use in mandatory, asynchronous AI literacy courses for diverse undergraduate populations. Addressing this gap, this study investigates a scalable GBL-based AI literacy course delivered to 4898 first-year undergraduates across disciplines. Using a mixed-methods design with 311 valid pre- and post-survey responses and 20 interviews, the study evaluates students' cognitive, behavioural, affective, and ethical learning of AI. Quantitative results show significant improvements in overall AI literacy across cognitive, behavioural, and affective dimensions, while ethical learning gains were not statistically significant. Qualitative findings suggest that GBL stimulated students' epistemic curiosity and engagement with AI ethics while revealing pedagogical, technical, and learner-centered challenges. The study provides large-scale empirical evidence and proposes an instructional design framework for scalable AI literacy integration in higher education.