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.

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).