As artificial intelligence (AI) technologies evolve and become increasingly embedded in everyday life, many educational systems worldwide have introduced AI as a subject of instruction. However, the pace and nature of AI’s evolution are uneven. In this dynamic context, learners must be equipped not only with foundational knowledge but also with the ability to transfer their understanding across domains and tasks, in order to adapt their judgement to new opportunities and limitations as they emerge. Transfer is a complex process that does not occur automatically, but can be supported through pedagogical strategies. Dissemination across subjects and diversification of learning context are two strategies that seems promising in this purpose. Among such strategies, dissemination across subjects and diversification of learning context have repeatedly demonstrated their effectiveness in fostering transfer in other educational domains. This doctoral work seeks to investigate whether these approaches can also support transfer in the context of AI education, with the aim of developing evidence-based pedagogical resources that enhance students’ adaptability to the evolving landscape of AI.