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The emergence of increasingly powerful AI technologies calls for the design and development of K-12 AI literacy curricula that can support students who will be entering a profoundly changed labor market. However, developing, implementing, and scaling AI literacy curricula poses significant challenges. It will be essential to develop a robust, evidence-based AI education research foundation that can inform AI literacy curriculum development. Unlike K-12 science and mathematics education, there is not currently a research foundation for K-12 AI education. In this article we provide a component-based definition of AI literacy, present the need for implementing AI literacy education across all grade bands, and argue for the creation of research programs across four areas of AI education: (1) K-12 AI Learning & Technology; (2) K-12 AI Education Integration into STEM, Language Arts, and Social Science Education; (3) K-12 AI Professional Development for Teachers and Administrators; and (4) K-12 AI Assessment.
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This content will become publicly available on April 7, 2026
A Case Study of Integrating AI Literacy Education in a Biology Class
Abstract Artificial intelligence (AI) has gained widespread public interest in recent years. However, as AI literacy remained excluded from the standard academic curricula, AI education in the US was predominantly offered through extra-curricular activities, which limited AI learning exposure to only a select group of students. Given these limitations, the need to integrate AI literacy education into the standard curricula is increasingly evident. This study investigated the integration of AI learning in an advanced biology course. Thirty-seven students participated in four lessons embedding AI learning in biology contexts. The interplay of students’ AI learning and biology knowledge was examined from the quantitative measure of conceptual understanding and qualitative analysis of interdisciplinary reasoning. This concurrent triangulation research design utilized results from both quantitative and qualitative analyses to develop a comprehensive understanding of students’ AI learning in the biology context. The results of the study showed a significant improvement in students’ AI concepts. Students’ biology knowledge had a slight increase, but it was not statistically significant. Both quantitative and qualitative results underscored a close connection between students’ AI learning and their biology knowledge, though the quantitative findings were not conclusive in some lessons. The article concluded with a discussion of the potential reasons for those discrepancies. In addition, suggestions were provided for future research and practitioners who are interested in integrating AI education across curricula.
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- PAR ID:
- 10585231
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- International Journal of Artificial Intelligence in Education
- ISSN:
- 1560-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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