- Can Tatar, Duncan Culbreth, Shiyan Jiang, C. Rosé, J. Chao, Rebecca Ellis, Shan Jiang, Kenia Wiedemann. 2022. High School Students’ Sense-making of Artificial Intelligence and Machine Learning. Abstract: This paper presents high school students’ sense-making of Artificial Intelligence (AI) and Machine Learning (ML) before and after they participated in a three-week technology-enhanced AI curriculum experience. We analyzed students’ pre-and-post assessment responses, responses to activity-specific questions, and classroom video recordings to explore their understanding of AI and ML. Our analysis revealed that students’ AI and ML conceptions shifted from media-informed concepts and presuppositions to more structured and process-oriented understandings after they completed AI curriculum modules. Our ongoing and future work aims to develop a deeper understanding of how students’ sense-making of AI and ML is influenced by their prior knowledge, experiences, and perspectives as well as the curriculum activities, tasks, and resources.
