Educators are facing the challenge of redesigning curricula to integrate artificial intelligence (AI) and machine learning (ML) methods in a way that is relevant and engaging to youth. Design-based research (DBR) presents a unique opportunity to conduct iterative re-design of these curricula while incorporating feedback from stakeholders including youth and professionals in the field. In this paper we present a mixed methods analysis of the iterative design of an informal science-integrated ML curriculum for high school youth enrolled in a four-week summer program. Each step of the two year DBR process was informed by input from our advisory board consisting of alumni and industry experts. Participants in both cohorts gained understanding in ML knowledge, with gains in Cohort 2 ( M 2 -M 1 = 0.175, p < 0.001, n=42) exceeding those of Cohort 1 ( M 2 -M 1 = 0.076, p < 0.001, n=35). Participants who identified as female and non-white tended to show greater learning gains than their white male counterparts. This project is an example of a successful participatory curriculum design process that centers youth voices in an advisory capacity, with implications for educational designers seeking to effectively integrate AI/ML into existing curricula. • Design-based research developed science secondary school machine learning curriculum. • Design incorporated youth and industry expert feedback through an advisory board. • Results show iterative improvements in understanding of machine learning knowledge.
Moore et al. (Sun,) studied this question.