AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review

Academic integrative review (higher education research)

International (K-12 and post-secondary literature)

University researchers synthesizing empirical and theoretical AI literacy studies

• In-school (K-12)
• Private program
• Informal learning

Integrative literature review (conceptual synthesis, not single intervention)

Studies published 2020 to July 2024; paper published 2025

124 reviewed studies

Not a single-site classroom deployment; tool usage varies across studies

• Review scope excludes some contexts (e.g., adult upskilling, teacher literacy, professional domains).
• Findings reflect conceptual patterns, not intervention effectiveness comparisons.
• Post-secondary empirical intervention evidence remains comparatively limited.

K-12 and undergraduate learners across included studies

Mixed and context-dependent across studies

Mixed; many interventions include beginner-accessible pathways

• Clarify how AI literacy is conceptualized across K-12 and higher education research.
• Identify shifts in AI literacy discourse after the rise of generative AI.
• Provide a unifying framework to improve precision in AI literacy design and communication.

• Map research gaps, especially around critical use of AI tools.
• Differentiate functional, critical, and indirectly beneficial literacy objectives.
• Support curriculum designers in aligning AI perspective with learning outcomes.

• Not a meta-analysis of intervention effect sizes.
• Not a single curriculum implementation report.
• Not an endorsement of one AI literacy framework only.

Framework-level synthesis of AI literacy approaches (technical AI, AI tools, sociocultural AI)

English peer-reviewed sources

• Evaluator

• Database search in ERIC and Scopus with purposive citation tracing.
• Apply inclusion and exclusion criteria for K-12 and undergraduate AI literacy studies.
• Extract definitions, motivations, and intervention goals.
• Synthesize themes into a conceptual framework.

• Transparent review period and search terms.
• Explicit criteria to separate learning about AI from learning with AI.
• Balanced inclusion of empirical and theoretical studies.

• Scope problem and establish integrative review rationale.
• Run systematic search and citation-based expansion.
• Screen and code studies for AI/literacy conceptualization.
• Construct framework and identify trends plus research gaps.

• Human researchers performed search design, screening, coding, and interpretation.
• AI is the literacy target in reviewed studies, not the autonomous reviewer.

• Use of established theoretical anchors (Dagstuhl Triangle, digital multiliteracies).
• Iterative bottom-up and top-down coding refinement.
• Cross-context comparison between K-12 and post-secondary studies.

• AI literacy remains an umbrella term with divergent interpretations.
• Tool-focused AI literacy often advances faster than critical literacy practices.
• Generative AI policy and age constraints complicate K-12 hands-on deployment.
• Post-secondary AI literacy work often lacks robust empirical classroom evaluation.

• Shift from primarily K-12 technical+ethics curricula to more post-secondary GenAI tool-use focus.
• Emergence of prompt engineering and effective AI-tool-use competencies.
• Growing integration of sociocultural and ethics discussions in multi-perspective curricula.
• Introduction of “indirectly beneficial” literacy framing (e.g., STEM interest, motivation).

• Strong publication growth in AI literacy, especially in 2023-2024.
• Marked increase in post-secondary studies after widespread generative AI adoption.

• Three AI perspectives (technical, tool, sociocultural) and three literacy perspectives (functional, critical, indirectly beneficial) consistently organize the field.
• Tool-perspective studies increased rapidly, often with functional objectives.
• Critical literacy around AI tools is identified as a major gap.

Use more specific labels than generic "AI literacy" when defining curricula and research objectives to improve clarity and comparability.

• Review emphasizes AI ethics concerns including bias, privacy intrusion, accountability, and societal impact.
• Responsible AI tool use requires explicit guidance and policy alignment in educational settings.
• Critical literacy is necessary to evaluate misinformation risks and unsafe over-reliance on generative AI outputs.

• Activity documentation
• Practitioner observation

• Offers a practical taxonomy for designing and classifying AI literacy interventions.
• Helps identify underexplored pairings, especially AI-tool perspective with critical literacy.
• Supports cross-context comparison between K-12 and higher education AI literacy strategies.

• AI literacy framework development
• Generative AI in education
• K-12 and higher-ed curriculum design
• AI ethics and sociotechnical learning outcomes

• Completed

K-12 and Undergraduate

School and higher education contexts

AI concept learning, tool use, and sociocultural analysis

Review synthesis of functional, critical, and indirect-benefit approaches

Medium

Low to Medium (literature synthesis; no single student dataset)