Research & Analysis
AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
By Xingjian (Lance) Gu and Barbara J. Ericson, University of Michigan School of Information, United States
Publication Date: March 2025
Executive Impact Summary
This integrative review synthesizes 124 studies (2020-2024) to establish a comprehensive framework for AI literacy, categorizing it by three AI perspectives (technical detail, tool, sociocultural) and three literacy perspectives (functional, critical, indirectly beneficial). It highlights a significant increase in AI literacy research, particularly in post-secondary contexts focusing on generative AI tools, and identifies key research gaps in critical and empirical studies.
0
Studies Reviewed (2020-2024)
0
Publications in 2024 (by July)
0
AI as Tool Studies in 2024 (by July)
0
K-12 AI Literacy Studies (since 2020)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
3
Perspectives on AI (Technical, Tool, Sociocultural)
3
Perspectives on Literacy (Functional, Critical, Indirectly Beneficial)
Integrative Review Process
Scoping & Problem ID
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Literature Search
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Data Evaluation
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Thematic Analysis
| Focus Area | Key Characteristics |
|---|---|
| Machine Learning in K-12 | Translates post-secondary ML curricula, functional literacy focus (e.g., computer vision tasks). |
| AI Ethics in K-12 | Views AI as sociotechnical systems, critical literacy focus (e.g., identifying biases in facial recognition). |
| Comprehensive K-12 Curricula | Combines technical AI details with sociocultural impacts, fostering both functional and critical literacy. |
| Post-Secondary (Pre-GenAI) | Limited empirical studies, often technical ML primers for non-CS majors; typically not labeled "AI literacy." |
DAILY Curriculum for Middle Schoolers
The Developing AI Literacy (DAILY) curriculum successfully introduced machine learning fundamentals, AI ethics, and career discussions to middle school students. An intervention with thirty-one middle school students (87% from underrepresented STEM groups) showed significant increases in ML knowledge, interest in AI careers, and AI ethical concerns. This approach effectively fostered both functional and critical AI literacy.
16+
Post-Secondary Generative AI Tool Use Studies (since 2023)
| Aspect | Focus / Objective |
|---|---|
| Functional Tool Use |
|
| Critical Tool Use |
|
K-12 Generative AI Integration
Despite age restrictions on direct generative AI tool use, K-12 research explores innovative ways to familiarize younger learners. This includes having kindergarteners interact with social robots to explore AI concepts or demonstrating generative AI tools to middle schoolers, showcasing issues like misinformation and biases. These efforts aim to build foundational understanding and critical awareness for responsible and ethical AI use.
Increased
Emergence of Indirect Benefits of AI Literacy
Beyond direct functional or critical skills, AI literacy research increasingly cites indirect benefits like increased STEM interest, improved computational thinking, and positive attitudes towards AI.
48+
AI Literacy Publications in 2024 (by July), Exceeding 2023 Total
Dominant
Growth of AI as Tool Perspective Since 2023
The conceptualization of AI literacy has rapidly shifted towards teaching the effective use of generative AI tools, especially in post-secondary settings.
| Gap Area | Description |
|---|---|
| AI Tools & Critical Literacy | Limited research on promoting critical literacy when interacting with AI tools and evaluating their outputs. |
| Post-Secondary Empirical Studies | Lack of empirical evaluations of AI literacy interventions in higher education contexts. |
| Technical & Tool AI Integration | Few studies combining AI's technical details with practical AI tool usage. |
| Specialized Terminology | Need for more precise terms beyond "AI literacy" to describe diverse objectives and approaches. |
Calculate Your Enterprise AI Readiness ROI
Estimate the potential savings and reclaimed hours by fostering AI literacy and integrating AI solutions within your organization.
Estimated Annual Savings
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Your AI Implementation Roadmap
Based on current research and industry best practices, here's a typical roadmap for integrating AI literacy within your enterprise.
Phase 1: Needs Assessment & Strategy Definition
Conduct a comprehensive review of existing skill sets and identify AI literacy gaps. Define clear, measurable objectives aligned with business goals and current AI literacy frameworks (functional, critical, sociocultural).
Phase 2: Curriculum Design & Pilot Programs
Develop tailored AI literacy programs, leveraging insights from K-12 and higher education models. Prioritize modules on AI fundamentals, ethical implications, and practical generative AI tool use. Implement pilot programs with diverse employee groups.
Phase 3: Broad Rollout & Integration
Scale AI literacy initiatives across relevant departments, integrating learning into daily workflows. Foster a culture of continuous learning and critical engagement with AI technologies across the organization.
Phase 4: Evaluation & Iteration
Regularly assess the impact of AI literacy programs on employee performance, innovation, and ethical AI deployment. Use feedback and performance metrics to iterate and refine programs, addressing emerging AI trends and challenges.
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