Design of a Science-Integrated Secondary School AI Literacy Curriculum: A Youth & AI Expert Guided Design-Based Research Approach

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Synthesis: Moore, Rabinowitz, Ali, Weckel, Lee, Gupta, and Chaffee (2026) present a mixed-methods analysis of the iterative design of an informal science-integrated machine learning curriculum for high school youth enrolled in a four-week summer program. Each step of the two-year design-based research (DBR) process was informed by input from an advisory board consisting of alumni and industry experts. Participants in both cohorts gained understanding in ML knowledge, with gains in Cohort 2 (M2-M1=0.175, p < 0.001, n = 42) exceeding those of Cohort 1 (M2-M1=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.

Key Findings

A two-year DBR process designed an informal science-integrated ML curriculum for high school youth, informed by a youth and AI-expert advisory board.
Participants in both cohorts gained ML knowledge, with Cohort 2 gains (M2-M1=0.175) exceeding Cohort 1 (M2-M1=0.076).
Participants who identified as female and non-White tended to show greater learning gains than their White male counterparts.
The project exemplifies a participatory curriculum design process that centers youth voices in an advisory capacity.
Findings have implications for educational designers seeking to integrate AI/ML into existing curricula.

Implications for AI in Education

The study demonstrates a successful participatory DBR approach to curriculum design for AI literacy in secondary education, centering youth voices in an advisory capacity. The finding that female and non-White participants showed greater learning gains suggests that well-designed, participatory AI curricula can advance equity in AI education. For curriculum designers, the science-integrated approach offers a model for embedding Machine Learning concepts within authentic disciplinary contexts rather than teaching them in isolation. The study connects to Science Education, AI Education, and Design Based Research research.

Connected Concepts

Connected Articles

[genai-literacy-training-teacher-education-dbr-2026] — DBR-based GenAI literacy teacher training
[ai-assisted-collaborative-learning-model-dbr] — DBR model for AI-assisted collaborative learning
[caruana-pre-university-ai-education-slr-2026] — systematic review of pre-university AI education
[liang-ai-learning-motivation-sdt-2026] — SDT analysis of students' AI learning motivation

Citation

Moore, K. S., Rabinowitz, G., Ali, S., Weckel, M., Lee, I., Gupta, P., & Chaffee, R. (2026). Design of a science integrated secondary school AI literacy curriculum: A youth & AI expert guided design-based research approach. Computers and Education: Artificial Intelligence, 10, 100552.

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