AI Education Across the Curriculum: Design and Pilot Study of a Cross-Disciplinary Module Set

Artificial intelligence (AI) education has garnered growing attention from both educational researchers and practitioners in recent years. Among the various emerging approaches, integrating AI education across the curriculum—particularly within core disciplines—offers distinct advantages. This strategy foregrounds the inherently interdisciplinary nature of AI and enables students to investigate its connections with subjects such as mathematics and English language arts (ELA). Furthermore, it holds promise for broadening participation by engaging all students, including those historically underrepresented and underserved in the field of AI. To date, most efforts to integrate AI education have been situated within individual classrooms, often led by a single teacher. While such initiatives provide valuable entry points, they overlook the reality that students’ learning experiences span multiple classrooms and disciplines. As students transition between subjects, they inevitably synthesize ideas—both consciously and unconsciously—from diverse instructional contexts. Recognizing this, we take a whole-school perspective that considers the cumulative and interconnected nature of students’ learning experiences. With this perspective, we explore a coordinated, cross-disciplinary approach in which students engage with AI through a set of curriculum modules spanning mathematics, ELA, and social studies. Each module is discipline-specific yet designed to contribute to a cohesive, cross-disciplinary exploration of AI. These modules are further framed by a self-paced introductory unit, which establishes foundational concepts, and a culminating application-and-reflection unit, which supports integration and transfer of learning. This paper describes the design of the AI Education Across the Curriculum module set and reports preliminary findings from a pilot implementation conducted in Spring 2025. By examining both the pedagogical design and initial findings, we aim to contribute to the growing body of research on scalable, equitable, and interdisciplinary models for AI education.