AI Literacy, Computational Thinking and Equitable Educational Preparedness in K-12 Settings
A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".
Deadline for manuscript submissions: 30 January 2027 | Viewed by 148
Editors
Interests: AI literacy in K-12 education; computational thinking for science (CT-S); sociotechnical AI learning design; equity-centered STEM education; professional learning for educators on AI; responsible AI curriculum development
Interests: computational thinking and science education integration; AI ethics and responsible AI curriculum; equity and broadening participation in STEM; computational modeling; K-12 AI literacy; problem-based learning design
Special Issue Information
Dear Colleagues,
Artificial intelligence is reshaping education at every level—from adaptive learning platforms and automated assessment to AI-powered career guidance and behind-the-scenes recommendation systems. However, students, teachers, and school leaders frequently encounter AI without knowing it is there, unable to interrogate the systems shaping their experiences. At the same time, AI is becoming indispensable to science and society writ large, creating urgent demands for learners who can engage with AI not merely as passive consumers, but as critically informed participants who can evaluate, question, and shape the technologies that surround them.
This Special Issue aims to present and disseminate research on how K-12 education can build the AI literacy, computational thinking, and sociotechnical competencies that students and educators need in an AI-pervasive world. We consider contributions addressing frameworks, curricula, pedagogical approaches, professional learning models, and equity considerations that support learners and educators in developing agency and criticality in relation to AI systems.
Topics of interest for publication include, but are not limited to, the following:
- Frameworks for K-12 AI literacy (conceptual, pedagogical, or assessment-oriented).
- Computational thinking as a lens for understanding and interrogating AI systems.
- Integrated approaches to AI ethics and technical learning (sociotechnical education).
- Equity-centered approaches to AI education and broadening participation.
- Invisible, rote, and reflective AI use in K-12 learning environments.
- Computational modeling and generative AI in science education.
- Professional learning models for teachers and district leaders on AI.
- Assessment of AI literacy competencies in K-12 contexts.
- District and systems-level strategies for responsible AI adoption.
- Student and educator agency in AI-mediated learning settings.
We look forward to receiving your contributions
Dr. Eric Greenwald
Dr. Ari Krakowski
Guest Editors
Manuscript Submission Information
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Keywords
- AI literacy
- computational thinking
- K-12 education
- equity
- AI ethics
- sociotechnical learning
- science education
- professional learning
- responsible AI
- invisible AI use
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