STEAM Education for AI Literacy: A Systematic Literature Review

Synthesis: This PRISMA systematic review of 39 studies (2016–2025) examines to what extent STEAM education advances AI literacy, using the TIECD framework refined into ten AI Literacy Elements (AILEs). It finds STEAM implementations chiefly develop technical literacies — fundamental AI concepts, Computational Thinking, data literacy — while underdeveloping ethical awareness, creative imagination, creating with AI, managing AI, and designing AI. Technology disciplines lead; arts, engineering, and integrated STEAM lag.

Key Findings

1. 39 studies, dual-level coding into ten AILEs. Following PRISMA, the review maps contributions to ten AI Literacy Elements across STEAM subjects and contexts, plus subject and STEAM-cluster mappings.
2. Sharp growth from 2021 onward. Publication on AI-as-content in STEAM increased markedly from 2021, concentrated in middle and high schools, with a geographically clustered corpus and predominantly mixed/qualitative methods using technology-enhanced instruction.
3. An uneven AI-literacy landscape. Technical foundations (Fundamental AI Concepts, Computational Thinking, Data Literacy) dominate, while Ethical Awareness, Creative Imagination, Creating with AI, Managing AI, and Designing AI are comparatively underrepresented.
4. Discipline-based contributions differ. Technology disciplines (computer science, data science) lead; science, mathematics, engineering, arts, and integrated STEAM have thinner coverage. Arts/humanities primarily support impact and ethics.
5. A revised AI-literacy framework. The review proposes an evidence-informed framework aligning TIECD with ten elements, recommending curriculum and assessment broadening so learners can not only use AI but shape it responsibly.

Implications

This review directly informs AI Literacy and Curriculum Design in K-12 STEM Education/STEAM: current STEAM implementations produce a lopsided, mostly-technical AI literacy. The neglected AILEs — ethical reasoning, creative futures thinking, collaborative management of AI, and designing AI systems — are exactly the higher-order competencies the wiki's critical-AI-literacy and Reducing AI Misuse strands emphasize. It offers a concrete element-based framework for balancing curriculum and assessment toward responsible AI shaping, not just technical skill.

Connected Concepts

• AI Literacy
• STEM Education
• Computational Thinking
• K 12
• Ethics
• Curriculum Design
• Equity In AI Education

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• Jiang Chatgpt Inquiry Steam Review 2026 — ChatGPT for inquiry-based learning in STEAM
• Young People Learning Generative AI Rapid Review 2026 — GenAI across learners, contexts, cultures
• Gaide Vibe Coding K12 Teachers — Vibe coding framework for K-12 teachers
• AI Literacy Heptagon 2026 — The AI Literacy Heptagon
• The Scaffolded AI Literacy Sail Framework Results Of A Delphi Study For Equitabl — The Scaffolded AI Literacy (SAIL) Framework

Citation

Niri, G., Chiu, T. K. F., Ombid, A. M. O., Ybañez, D. L. J. B., Dennerlein, S. M., Zhou, X., & Lavicza, Z. (2026). STEAM education for AI literacy: a systematic literature review. International Journal of STEM Education, 13, 46.