DOI
https://doi.org/10.36837/chapman.000762
Abstract
This conceptual paper explores the intersection of computational thinking (CT) and artificial intelligence (AI) literacy, proposing a framework integrating AI literacy into existing computational literacy models. We argue that computational thinking (CT) skills form the foundation for AI literacy, and that lessons learned from decades of computer science (CS) education research can ably guide the development of AI literacy instruction for a wide range of students and within integrative contexts. Drawing on Kafai and Proctor's (2022) conceptualization of computational literacy, as well as encompassing cognitive, situated, and critical dimensions, we extend their framework to advance our understanding of AI literacy. We examine theoretical underpinnings of computational literacy and explore its three strands as applied to AI literacy, highlighting the potential for mutual learning between AI literacy and CS education. This paper aims to provide researchers, educators, and policymakers with a principled framework for implementing AI literacy initiatives in schools and classrooms.
Publication Date
2026
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Jacob, Sharin; Prado, Yenda; and Burke, Quinn
(2026)
"Computational and AI Literacy: Lessons Learned from Computer Science Education,"
Journal of Computer Science Integration: Vol. 8:
Iss.
1, Article 1.
Available at:
https://digitalcommons.chapman.edu/jcsi/vol8/iss1/1