Framing AI Literacy for K-12 Education: Insights from Multi-Perspective and International Stakeholders

Academic and policy-oriented multi-stakeholder research

International (Nordics, Europe, and global expert representation)

Interdisciplinary researchers eliciting and synthesizing expert perspectives

• In-school (K-12)

Qualitative survey-based framing study for curriculum-level competencies

Survey collection and thematic analysis cycle (published 2025)

33 international experts (from 40 invited participants who accepted)

Online survey instrument with open-ended competency framing prompts

• No global consensus yet on K-12 AI literacy competencies and progression depth.
• AI literacy definitions vary by context, age, role, and stakeholder perspective.
• Rapid AI evolution challenges stable curriculum framing and assessment design.

K-12 learners (framework-level target)

Growing everyday exposure to AI-enabled systems across contexts

Not necessarily required for baseline AI literacy

• Frame core AI literacy competencies suitable for K-12 education.
• Integrate both technological understanding and socio-cultural awareness.
• Clarify what students should know to critically and responsibly engage with AI.

• Distinguish competencies for understanding, using, evaluating, and shaping AI.
• Support curriculum design that reflects diverse stakeholder perspectives.
• Identify priority competency gaps for future educational policy and practice.

• Not a single-country curriculum standard.
• Not a classroom intervention trial with student outcome metrics.
• Not a finalized universal definition of AI literacy.

Framework and competency elicitation for AI literacy in K-12

English and Nordic languages for expert responses

• Evaluator

• Experts answered open-ended prompts on understanding, using, and developing AI in K-12.
• Researchers conducted inductive content analysis to derive competency categories.
• Supplementary deductive profiling compared technological and socio-cultural emphasis.
• Results were iteratively reviewed across multiple coders and disciplinary perspectives.

• Anonymized survey data handling with GDPR-aligned storage and processing.
• Inter-coder checks and iterative consensus procedures to reduce subjective bias.
• Careful reporting to protect participant confidentiality and role anonymity.

• Define expert panel criteria and recruit interdisciplinary stakeholders.
• Collect open-ended responses on key K-12 AI literacy dimensions.
• Perform iterative inductive coding and category synthesis.
• Profile individual response emphases across technical and socio-cultural perspectives.

• Human experts supply domain judgments on needed K-12 AI competencies.
• Human researchers conduct coding, reliability checks, and interpretive synthesis.
• AI systems are discussed as educational object/context, not autonomous analyst.

• Prompt design aligned with literacy dimensions (understand, use, develop).
• Multi-pass coding and reviewer triangulation for thematic robustness.
• Category construction balancing conceptual, ethical, reflective, and design facets.

• Unclear boundaries between required technical depth and practical AI use skills.
• Tension between AI as black-box reality and need for student agency.
• Difficulty operationalizing balanced competencies across age levels in K-12.
• Variation in stakeholder priorities complicates standard-setting efforts.

• Framed AI literacy across four integrated categories rather than one-dimensional skill lists.
• Included both technological and socio-cultural coding lenses for nuanced interpretation.
• Added response-profile analysis to capture stakeholder emphasis diversity.
• Explicitly treated bias as both technical and socio-cultural phenomenon.

• Stakeholder responses showed broad engagement with both practical and ethical AI concerns.
• Cross-role input surfaced actionable tensions for curriculum design.

• Four core competency domains emerged: conceptual knowledge, ethics/society, critical reflection, and design/development.
• Socio-cultural competencies were slightly more emphasized overall than purely technical ones.
• Many experts viewed technical understanding as prerequisite for responsible and critical AI participation.

A balanced K-12 AI literacy framing should combine foundational technical understanding with critical socio-cultural reasoning, rather than prioritizing either dimension in isolation.

• Ethical AI literacy themes included fairness, transparency, accountability, bias, and sustainability.
• Students should learn when AI is acting on people, not only when users actively invoke tools.
• Responsible participation requires critical evaluation of data sources, system limitations, and social consequences.

• Practitioner observation
• Activity documentation

• Supports competency-framework development for K-12 AI literacy across contexts.
• Provides evidence for integrating socio-cultural and technical dimensions in curriculum policy.
• Offers categorized stakeholder insights for designing age-appropriate AI learning progressions.

• AI literacy framework design
• K-12 curriculum policy and standards
• Ethics and sociotechnical AI education
• Interdisciplinary digital competency research

• Completed

K-12 (framework-level applicability)

Curriculum and policy framing context

Understanding, evaluating, and responsibly using AI in everyday life

Competency framing through interdisciplinary stakeholder synthesis

Medium

Low (anonymized expert survey data)