Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers - Xie, Benjamin; Sarin, Parth; Wolf, Jacob; Garcia, Raycelle C. C.; Delaney, Victoria; Sieh, Isabel; Fuloria, Anika; Varuvel Dennison, Deepak; Bywater, Christine; Lee, Victor R. (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
High school teachers from many disciplines have growing interests in teaching about artificial intelligence (AI). This cross-disciplinary interest reflects the prevalence of AI tools across society, such as Generative AI tools built upon Large Language Models (LLM). However, high school classes are unique and complex environments, led by teachers with limited time and resources with priorities that vary by class and the students they serve. Therefore, developing curricula about AI for classes that span many disciplines (e.g. history, art, math) must involve centering the expertise of cross-disciplinary teachers. In this study, we conducted five collaborative curricular co-design sessions with eight teachers who taught high school humanities and STEM classes. We sought to understand how teachers considered AI when it was taught in art, math, and social studies contexts, as well as opportunities and challenges they identified with incorporating AI tools into their instruction. We found that teachers considered technical skills and ethical debates around AI, opportunities for "dual exploration" between AI and disciplinary learning, and limitations of AI tools as supporting engagement and reflection but also potentially distracting. We interpreted our findings relative to co-designing adaptable AI curricula to support teaching about and with AI across high school disciplines.