Bridging the AI Literacy Chasm: Initial Insights from “Project X: Algebra Engineering Lab” in Baltimore City High Schools
Abstract
In May 2025, the OECD, European Commission, and Code.Org released a new framework for integrating Artificial Intelligence (AI) Literacy into K-12 education (OECD et al., 2025). Similar to the rollout of Next Generation Science Standards over a decade ago, this AI Literacy Framework propels a vision of competencies to develop and ideals to aspire to—without a concrete curriculum for practitioners to use. Moreover, troubling signs indicate that differential training and resource access to AI literacies can exacerbate educational inequality (Lake, 2024). For instance, suburban, majority-white, and low-poverty school districts were about twice as likely to provide AI-use training to their teachers than urban, rural, or high-poverty districts (Diliberti et al., 2024). This poster highlights emergent work from the Baltimore Online Algebra for Students in Technology (BOAST) project, a larger NSF-funded program and research project (DRL-2005790) developed by Johns Hopkins’ Center for Educational Outreach in partnership with Baltimore City Public School District’s (City Schools) mathematics office. The program’s curriculum entails online learning with math/algebra (reinforcement) lessons contextualized within engineering challenges (i.e., “missions”). The curriculum was developed and tested in three afterschool cohorts (2021-2024) and one school day elective class (Fall 2024). Specifically, this poster highlights the “Machine Learning” mission, in which students first work through the K Nearest Neighbor (KNN) calculations using the Euclidean distance method. Then, students select variables and k-values to test KNN calculations on a large sample weather data set. This mission also involves the creation of digital models capable of distinguishing between images of cats and dogs using Teachable Machine, a free web-based tool by Google.
This study utilizes triangulated data between student satisfaction surveys, student focus groups, and instructor interviews to outline student perceptions of the machine learning unit. The sample included 39 students (grades 10-12) enrolled in the “Project X: Algebra Engineering Lab” for-credit elective class in their schools. As a class pre-requisite, students were required to have passed Algebra I with a final report card grade of C- or better. Students reflected demographics of the general school district: 70% African American, 20% Hispanic/Latino, and 7% White; 16% multilingual learners; and 76% low-income (Baltimore City Public Schools, 2025). Institutional Review Boards at City Schools and JHU reviewed and approved this study.
Findings reveal that the Machine Learning mission allowed students to see how traditional mathematical topics (e.g., the Pythagorean theorem) are connected to contemporary technologies. Students were introduced to fundamental AI concepts such as pattern recognition, facial analysis, and algorithmic bias. While student enjoyment was only moderate (average 3 on a 5-point Likert scale), students evidenced AI literacy in the domains engaging with AI and designing AI: “I liked the mission very well. Especially since I was able to learn that machines can adapt to our knowledge if we teach them how.” While some students reported periodic confusion with the concepts explored in this mission, others highlighted how the activities helped them develop understanding of how AI could amplify societal biases: “I learned how AI face recognition works with different people of color.” Future directions for integration of all AI literacy domains (refer to Figure 1 and 2) into City Schools are considered.
References
Baltimore City Public Schools. (2025). District Data Profile. District Overview. https://www.baltimorecityschools.org/page/district-overview/
Diliberti, M. K., Schwartz, H. L., Doan, S. Y., Shapiro, A., Rainey, L. R., & Lake, R. J. (2024). Using Artificial Intelligence Tools in K-12 Classrooms. https://www.rand.org/pubs/research_reports/RRA956-21.html
OECD, European Commission, & code.org. (2025, May). Empowering Learners for the Age of AI An AI Literacy Framework for Primary and Secondary Education. https://ailiteracyframework.org
Lake, R. (2024, May). AI is coming to U.S. classrooms, but who will benefit? CRPE.