Work

Extending Computer Science to AI Literacy: The Case for Data Science Education

Year: 2026

Type: article

Abstract: As artificial intelligence (AI) becomes increasingly embedded in schools, workplaces, and everyday digital systems, K-12 computer science education faces a growing question: how should students be taught about AI and prepared to engage with it? AI literacy frameworks emphasize competencies beyond programming and algorithmic thinking, including understanding how AI systems operate, evaluating their outputs, and recognizing their limitations and ethical implications. This article develops a conceptual argument that data science education offers an important pathway for preparing students for AI because many contemporary AI systems are grounded in data. Drawing on literature in AI literacy, computer science education, and data science education, as well as our research on high school data science curricula and implementation in Utah and Virginia, this article examines how data science is emerging across courses, curricula, and state-level initiatives. The analysis highlights variation in instructional emphasis, course placement, student access, and teacher preparation needs. It argues that data science can extend computer science education by creating interdisciplinary, data-centered opportunities for students to encounter computing concepts in applied contexts and develop the foundational understanding needed to engage critically and responsibly with AI.

Source: Education Sciences

DOI: 10.3390/educsci16091369

Authors: Joanna Schiffman, Dana Elobaid, Huifang Zuo, Kelly Smalenberger, Jeanne Century

Institution: University of Chicago

Language: English

Topic: Teaching and Learning Programming

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,172