- J. Chao, W. Finzer, C. Rosé, Shiyan Jiang, Rebecca Ellis, Kenia Wiedemann, Can Tatar, James Fiacco. 2022. Teach Artificial Intelligence with StoryQ, A Web-Based Machine Learning and Text Mining Tool for K-12 Students. Abstract: 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).
