The expedited adoption (or at least the use) of generative AI technologies has revolutionised various sectors, with informatics education being to the fore with respect to both practice and research. However, this narrow focus comes at a cost to the wider AI in and for education research space. This working group proposal aims to explore the current trends and systematically explore multiple sources of information to identify areas of AI research in K-12 informatics education that are being underserved and needed in the post-generative AI phase era. Our research focuses on three primary areas: curriculum, teacher-professional learning and policy. The denouement of this aims to identify trends and shortfalls for AI in and for K-12 informatics education.

We will first examine the current literature (using a systematic literature review) to identify themes and subsequently trends in AI education at K-12. This will be under two facets, curricula and teacher-professional learning. Secondly, we will conduct interviews with educators and AI experts to ensure that we have not omitted themes from our analysis. Thirdly we will then examine the current policy (such as the European AI Act, and European Commission guidelines on the use of AI and data in education and training as well as international counterparts), as often policy is developed by both educators and experts in the domain, thus providing a source of topics or areas that may be of value that are not currently identified in the literature and curricula. Finally, by synthesising insights from educators, AI experts, and policymakers, as well as the literature and policy, our working group seeks to highlight possible future trends and shortfalls.

Our focus on life after generative AI is both timely and crucial. In an era of research where generative AI and Large Language models dominate the research proceedings (in many cases focusing on assessment and pedagogies for teaching traditional informatics subjects such as introductory programming), it is time to step back and examine the wider picture of AI in and for K-12 education, to identify our shortfalls and strengths as a computing research community, to support and inform future research and activities.