AI Literacy Frameworks

Explore AI Literacy is a visual navigator for AI literacy frameworks. Use interactive tools to explore how different frameworks define and emphasise AI literacy across 9 domains.

Methodology adapted from the HGSE EASEL Lab Taxonomy Project. Domain structure informed by the Big-AL framework (McMinn, 2026, work in progress).

Each framework is coded against 9 domains capturing the full breadth of AI literacy — from technical skills to socio-political engagement.

Framework Overview

Each radar chart shows a framework's emphasis across 9 domains. Click any card to view the full profile.

Domain Focus Explorer

Compare how frameworks distribute emphasis across the 9 AI literacy domains. Select up to 3 frameworks to overlay on the radar chart.

Radar Comparison

Select frameworks to compare their domain emphasis

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All Frameworks by Domain

Compare how frameworks distribute emphasis across domains

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General Focus: Relative Values Shows how much emphasis each framework places on one domain relative to others. Useful for understanding a framework's general focus.

Depth of Focus: Absolute Values Shows how many constructs each framework maps to each domain. Useful for understanding the depth and scope of coverage.

DOMAINS

Sort by Domain

Compare Constructs Between Frameworks

Different frameworks may refer to the same or similar skills using different names. Select a framework from each list to see where constructs in the left framework are related to constructs in the right one — based on shared underlying skills rather than names alone. The thicker the line, the more skills they share.

Select two frameworks to compare their constructs

Framework Profiles

Detailed profiles for each AI literacy framework, including metadata, domain distributions, and key constructs.

Gap Analysis

Identify underrepresented domains in each framework. Domains scoring below 5% are flagged as coverage gaps.

Interpreting the gap zones: Domains 1–4 (Technical Understanding, Practical Application, Critical Evaluation, Ethical Reasoning) represent functional, competency-based literacy — often called "little-al." Domains 5–9 (Societal Awareness, Human Agency, Governance, Cognitive Processes, Sociocultural Orientation) represent socio-technical, critical, and governance dimensions — "Big-AL." Most existing frameworks concentrate heavily on Domains 1–4.

Gap Radar

Domains below 5% are highlighted as gaps. The grey line shows the average across all frameworks.

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Domain Scores Across All Frameworks

Cross-Framework Skills Map

Explore how different AI literacy frameworks name and group related skills. The sunburst shows common skills (middle ring) mapped to framework-specific terms (outer ring), organized by the 9 domains (inner ring). Inspired by the HGSE EASEL Lab methodology.

AI Literacy

Click a ring to explore. Inner = domains, middle = common skills, outer = framework terms.

Framework Terms

Big-AL Lens

How well does each framework address the five Big-AL literacies? This heatmap computes alignment scores by mapping each literacy to its constituent domains. Red shading indicates stronger coverage; blue indicates weaker coverage.

Landscape Diagnosis

A data-driven analysis of where 20 AI literacy frameworks converge, diverge, and leave gaps — computed live from the framework data below.

Domain Coverage: Range & Emphasis

Each row shows one domain. The line spans from the minimum to maximum coverage across all 20 frameworks. The dot marks the mean. Wide spans reflect differences in depth or scope — often driven by audience and purpose — while narrow spans suggest the domain carries similar weight regardless of context.

Key Findings

Audience Blind Spots

Which populations have dedicated AI literacy frameworks — and which don't?

Implications for Big-AL

Low (<7)

Mid (7–12)

High (>12)

Select a cell for details

How Alignment Scores Are Computed

Each Big-AL literacy maps to two primary domains (weighted at 40% each) and one secondary domain (weighted at 20%). The alignment score is the weighted sum of a framework's domain percentages for those domains, normalised to a 0–100 scale relative to the maximum achievable score. The composite score is the average of all five literacy scores.

About & Methodology

This tool provides a structured comparison of AI literacy frameworks using a domain-based coding methodology adapted from the Harvard Graduate School of Education EASEL Lab.

The 9 Domains

Each framework is coded against 9 domains that capture the full breadth of AI literacy — from technical skills to socio-political engagement. The domain structure is informed by the Big-AL framework (McMinn, 2026, work in progress).

Coding Methodology

The coding approach adapts the HGSE EASEL Lab Taxonomy Project methodology, originally developed for social-emotional learning frameworks, to the domain of AI literacy.

Each framework's stated competencies, skills, and learning outcomes were mapped to the 9 domains. Weights reflect the relative emphasis each framework places on each domain, based on the proportion of constructs, detail, and stated importance allocated. Domain weights for each framework sum to approximately 100%.

Key Constructs

Key constructs are the named skills, competencies, or abilities that each framework identifies. Each construct is tagged with its primary domain mappings, indicating which domains it primarily addresses.

Construct ↑	Framework ↑	Mapped Domains ↑

Literacy vs. Literacies

A note on terminology: Some frameworks use the singular "AI literacy," framing it as a unified set of functional competencies — understanding, using, and evaluating AI. Others adopt the plural "AI literacies," drawing on New Literacy Studies (Street, 1984; New London Group, 1996) to emphasise that being literate with AI is not a single skill but a constellation of socially situated, culturally shaped practices. The Big-AL framework formalises this distinction: "little-al" refers to individual competencies in Domains 1–4 (Technical Understanding through Ethical Reasoning), while "Big-AL" captures the socio-technical and critical practices in Domains 5–9. The chart below positions each framework on this spectrum based on its domain emphasis.

Theoretical Alignments

Dagstuhl Triangle — Frames digital literacy through three interconnected perspectives: technological, societal, and application-oriented.
Street's Ideological Model of Literacy — Argues that literacy is always embedded in social and power structures, never autonomous or neutral.
Scribner's Literacy Metaphors — Conceptualises literacy as adaptation (functional skills), power (critical agency), and state of grace (personal enrichment).
Big-AL Framework (McMinn, 2026, work in progress) — A framework under development at HKUST for faculty AI literacy development. Distinguishes between "little-al" (individual competencies in Domains 1–4) and "Big-AL" (socially situated, critical practices in Domains 5–9).

Data Sources

The 20 frameworks analysed were selected for their influence, recency, and diversity of perspective. They range from K-12 curriculum standards to higher education competency models, workforce development frameworks, healthcare professional competencies, and critical literacy frameworks. Full citations and source URLs are available in each framework's profile card.

Limitations

Domain coding involves interpretive judgement. Different coders may assign slightly different weights. This tool represents one systematic reading of each framework's emphasis areas. The domain structure itself reflects a particular theoretical orientation (Big-AL), which foregrounds socio-technical and critical dimensions that some frameworks intentionally de-emphasise.

Timeline Evolution

How AI literacy frameworks have emerged over time, and how domain emphasis has shifted.

Domain Emphasis by Year

Average domain weights for frameworks published in each year. Shows how the field's emphasis has shifted.

Side-by-Side Compare

Select two frameworks to compare their domain profiles, features, and constructs.