Throughout the training, educators will examine how the curriculum bridges traditional computational thinking, or CT 1.0, with machine learning-enhanced thinking, or CT 2.0. Participants will learn how to support students as they build Python programming skills, work through the complete data science cycle, analyze real-world datasets, investigate classification algorithms, examine algorithmic bias, and implement machine learning models using industry-standard Python libraries such as pandas, scikit-learn, and Keras.

The course includes hands-on practice with core instructional activities from the curriculum, including domain-specific data projects, unplugged classification activities using the Titanic dataset, technical model-building exercises, and an MNIST digit recognition project. Participants will also consider how to facilitate student projects in science, business, engineering, healthcare, economics, environmental science, sports analytics, and social media wellness.

Emphasis is placed on helping teachers develop both the technical confidence and instructional strategies needed to teach AI responsibly. Participants will examine how to integrate critical data literacy, ethical reasoning, algorithmic bias, and the societal impacts of AI systems throughout instruction. The course also highlights alignment with Virginia’s Data Science Standards of Learning and CSTA Computer Science Standards.

By the end of the professional development experience, participants will be prepared to implement the AI/Machine Learning curriculum in a high school setting and support students in becoming technically capable, critically aware, and ethically responsible participants in an AI-driven world.