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.

Developing the ability to think critically about AI and interpret its outputs requires an understanding of AI bias, a key skill for both AI users and future developers. While some initiatives have introduced teens to algorithmic bias, few have engaged them in actively identifying and quantifying bias in real-world generative AI systems. This paper presents BiasViz, an interactive tool that leverages project-based and narrative-centered learning to help middle school students (11-14 year old) analyze AI bias in large language models. We conducted a study of 28 students’ interactions with BiasViz to evaluate its efficacy in fostering critical thinking about AI bias. Our findings suggest that BiasViz successfully introduced most students to AI bias, and some used the tool to explore personally relevant biases. We identify opportunities for the tool’s iteration and associated curriculum to promote learning and share insights for designing learning environments that foster youth’s critical thinking about AI.