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.

As artificial intelligence (AI) becomes increasingly embedded in scientific and technical domains, the ability to engage in AI-integrated STEM problem-solving is emerging as a critical skill for the future STEM workforce. Supporting students in this type of problem-solving requires building a strong foundation in computational thinking, particularly through pedagogically effective and technically robust tools. In this paper, we propose augmenting i-Sail, a block-based programming environment designed for AI-integrated STEM problem-solving, with large language model-driven feedback capabilities to facilitate students' problem-solving while reinforcing key computational thinking skills for middle-grade students. We prompt a large language model with structured knowledge about breadth-first search to provide contextualized, adaptive feedback. The LLM helps students connect their problem-solving steps to the high-level structure of the breadth-first search algorithm and apply this understanding to pathfinding. We present a proof-of-concept evaluation that demonstrates the potential of the system to support the development of computational thinking through AI-integrated problem solving in diverse STEM contexts.

Exploring Artificial Intelligence (AI) in English Language Arts (ELA) with StoryQ is a 10-hour curriculum module designed for high school ELA classes. The module introduces students to fundamental AI concepts and essential machine learning workflow using StoryQ, a web-based GUI environment for Grades 6-12 learners. In this module, students work with unstructured text data and learn to train, test, and improve text classification models such as intent recognition, clickbait filter, and sentiment analysis. As they interact with machine-learning language models deeply, students also gain a nuanced understanding of language and how to wield it, not just as a data structure, but as a tool in our human-human encounters as well. The current version contains eight lessons, all delivered through a full-featured online learning and teaching platform. Computers and Internet access are required to implement the module. The module was piloted in an ELA class in the Spring of 2022, and the student learning outcomes were positive. The module is currently undergoing revision and will be further tested and improved in Fall 2022.

StoryQ is a web-based machine learning and text mining tool that allows young learners (Grade 6-12) to engage in machine learning practices and work with unstructured text data without needing to code. StoryQ features dynamically linked data representations that promote meaningful inquiries and understandings across tables, graphs, and texts. These links create a unique user experience that makes machine learning models transparent, explainable, and fun to explore. This demo will showcase how key AI concepts such as representation, reasoning, feature space, feature weight, and machine learning are dynamically visualized in StoryQ and made accessible to young learners. A brief tutorial will be provided on how to use StoryQ to train, test, and troubleshoot text classification models using both standard feature extractors (e.g., N-grams) and special feature extraction tools and visualizations that have been specially designed to support young learners and non-computing teachers. This demo will also include sample learning activities designed for high school English Language Arts and History classes to showcase how machine learning concepts and practices can be introduced in non-computing classes. As the demands for AI scientists, engineers, and entrepreneurs have increased in recent years, as well as AI's increased presence in everyday lives, making access to how machine learning practices work is of paramount importance for young learners. This work is supported by an NSF ITEST project (DRL-1949110).

As AI becomes more widely used across a variety of disciplines, it is increasingly important to teach AI concepts to K-12 students in order to prepare them for an AI-driven future workforce. Hence, educators and researchers have been working to develop curricula that make these concepts accessible to K-12 students. We are designing and developing a comprehensive AI curriculum delivered through a series of carefully crafted activities in an adapted \emph{Snap!} environment for middle-grade students. In this work, we lay out the proposed content of our curriculum and present the design, development, and implementation results of the first unit of our curriculum that focuses on teaching the breadth-first search algorithm. The activities in this unit have been revised after being piloted with a single high-school student. These activities were further refined after a group of K-12 teachers examined and critiqued them during a two-week professional development workshop. Our teachers created a lesson plan around the activities and implemented that lesson in a summer workshop with 14 middle school students. Our results demonstrated that our activities were successful in helping many of the students in understanding and implementing the algorithm through block-based programming while extra supplementary material was needed to assist some other students. In this paper, we explain our curriculum and technology, the results of implementing the first unit of our curriculum in a summer camp, and lessons learned for future developments.

The StoryQ environment provides an intuitive graphical user interface for middle and high school students to create features from unstructured text data and train and test classification models using logistic regression. StoryQ runs in a web browser, is free and requires no installation. AI concepts addressed include: features, weights, accuracy, training, bias, error analysis and cross validation. Using the software in conjunction with curriculum currently under development is expected to lead to student understanding of machine learning concepts and workflow; developing the ability to use domain knowledge and basic linguistics to identify, create, analyze, and evaluate features; becoming aware of and appreciating the roles and responsibilities of AI developers;. This paper will consist of an online demo with a brief video walkthrough.

StoryQ is a web-based machine learning and text mining tool that allows young learners (Grade 6-12) to engage in machine learning practices and work with unstructured text data without coding. StoryQ features dynamically linked data representations that promote meaningful inquiries across tables, graphs, and texts--a unique user experience that makes machine learning models transparent, explainable, and fun to explore. This demo provides a brief tutorial on how to use StoryQ to train, test, and troubleshoot text classification models using both standard feature extractors (e.g., N-grams) and special feature extraction tools and visualizations designed to support young learners and non-computing teachers. This demo also includes sample learning activities designed for high school English Language Arts classes to showcase how machine learning concepts and practices can be introduced in non-computing classes. This work is supported by an NSF I-TEST project (DRL-1949110).

Augmented reality (AR) has great potential to radically change science education by making abstract science concepts visible and interactive. In this paper, we describe initial investigations into high school students' perceptions of learning science with an AR technology (i.e., SmartIR) through analyzing semi-structured interviews. SmartIR is an app that supports the investigation of science, such as thermodynamics. Specifically, it can show changes in thermal imaging over time and provides a data analytics function that visualizes data for analyzing and interpreting the changes. Our analysis of 31 interviews shows that students perceived the exploration of science phenomena with "fine details", including a full vision of second-by-second changes in thermal imaging, as helpful and engaging to understand science concepts. In future work, these findings will be triangulated with logging data of their interactions with SmartIR and student-generated lab reports.