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.

Multiple-choice questions (MCQs) are central to instruction and assessment, with distractors revealing student understanding and misconceptions. However, creating high-quality distractors is time-consuming, especially for emerging domains like K–12 AI education. This study explores using generative AI to support distractor creation in a self-paced online module integrating AI and Algebra 1. Five MCQs were selected to compare distractors written by human developers and ChatGPT, using expert reviews and log data from 80 students. Experts rated human distractors higher overall, though AI ones consistently ranked second. Log analysis showed human distractors drew more initial selections, while students who chose AI distractors spent more time engaging without differences in hint use or revisits. Transition patterns across attempts suggest AI-generated distractors can effectively guide students toward correct answers, highlighting their potential for scalable MCQ design.

Artificial Intelligence (AI) literacy is increasingly recognized as a critical competency for K–12 students, yet little is known about how learners engage with AI-focused modules in virtual school contexts. To address this gap, we designed an online narrative-driven AI literacy module (AI4VS) that integrates AI learning with Algebra 1. In this pilot study, data from 80 secondary school students who completed the 250-minutes module in three weeks were analyzed, including 117,866 system log records (e.g., submissions, clicks) and pre-/post-surveys on mathematics motivation, AI self-efficacy, and AI literacy. Using K-means clustering, we identified four distinct behavioral patterns: reflective learners, low-revision committers, high-frequency trial-and-error learners, and balanced learners. These groups demonstrated different outcomes: while all clusters showed significant improvement in AI self-efficacy, only some showed notable gains in motivation (i.e., low-revision committers and balanced learners) and AI literacy (i.e., balanced learners). The findings underscore the need for tailored scaffolds to better support varied learning strategies and highlight the potential of the AI literacy module in accommodating diverse learner profiles.

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).

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).

Using longitudinal survey data from women in the CRA-W Graduate Cohort program, we measured the prevalence of observed or experienced sexism and its impact on departure from Computer Science and Computer Engineering (CSE) doctoral programs. Our data suggest that sexist behavior is perceived less often by these women than it is by women in general. In addition, few of the women who observe sexism are motivated by it to think of leaving their CSE doctoral programs. Nevertheless, when their reason for thinking of leaving is due to sexism they observed or experienced, the odds of women actually departing are at least 21 times greater than if they thought of leaving for any other reason.