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Artificial Intelligence Literacy and Sustainable Development: An Ethical Governance and Development Goals Framework

created 2026-09-10 · updated 2026-09-19

Synthesis: Islam, Morshed, and Islam reconceptualize AI Literacy not merely as an educational or technical skill but as a AI Governance capacity that supports all 17 UN Sustainable Development Goals. They introduce a six-level AIRE (Artificial Intelligence Reasoning and Ethics) Taxonomy extending Bloom's hierarchy with ethical judgment and strategic foresight, plus an AI–SDG Nexus Framework that maps literacy competencies onto each goal. A survey of 300 professionals in a national context found strong technical awareness but limited ethical and AI Governance readiness, with ethical reasoning, reflective thinking, and governance literacy emerging as the strongest predictors of sustainable and trustworthy AI use.

Introduction

The paper addresses a gap: existing AI-literacy research treats the construct as an educational or technical concept rather than a systemic governance mechanism that ensures ethical alignment across all seventeen Sustainable Development Goals. By linking AI Literacy to Ethics and AI Governance, the authors position literate publics as a precondition for responsible AI deployment.

The AIRE Taxonomy and AI–SDG Nexus

The study's central contribution is the AIRE Taxonomy, a six-level model extending Bloom's learning hierarchy with ethical reasoning and strategic foresight:

  1. Recognize – Awareness: identify AI concepts, common tools, and use cases.
  2. Comprehend – Understanding: explain how AI systems function, their data structures, and limits.
  3. Apply – Responsible Use: use AI tools effectively and ethically.
  4. Analyze – Critical Evaluation: evaluate AI outputs for accuracy, bias, and appropriateness.
  5. Integrate – Ethical Synthesis: combine ethical judgment with technical understanding.
  6. Govern – Strategic Foresight: align AI use with institutional and societal goals.

The AI–SDG Nexus Framework systematically maps these AI-literacy competencies onto all seventeen SDGs, and the authors describe AI literacy as an integrative, cross-cutting "18th SDG" heuristic — a cognitive and ethical bridge that channels learning into governance and sustainable development. They distinguish foundational literacy (Recognize–Apply) from advanced competency (Analyze–Govern), preserving the conventional meaning of literacy while extending it into a structured developmental pathway that supports Curriculum Design and institutional policy.

Empirical Findings

A survey of 300 participants from diverse professional backgrounds within a national context measured AI-literacy readiness across four domains (Technical & Cognitive, Ethical & Reflective, Socio-Civic & Governance, Sustainability & Risk). Key results:

  • Technical awareness of AI was relatively strong, but ethical reasoning and AI Governance literacy remained limited — revealing gaps in public capacity to manage AI responsibly.
  • Ethical reasoning and reflective thinking were the strongest predictors of sustainable and trustworthy AI use.
  • In regression analysis, governance literacy was the strongest predictor of nexus awareness (β = 0.64, p < 0.01), followed by ethical and technical literacy.
  • Governance literacy showed the strongest association with Nexus Awareness (r = 0.67, p < 0.01).
  • Stakeholders prioritized actions aligned with SDG 16 (public awareness 24.5%, government policy 18.8%), SDG 4 (curriculum training 21.5%), and SDG 9 (industry partnerships and innovation labs).

The authors conclude that literacy-based competencies should be embedded into curricula, institutional policies, and governance mechanisms to accelerate equitable and responsible progress toward the Sustainable Development Goals.

What this means for practice

  • Curriculum designers. Sequence AI instruction along the AIRE ladder rather than stopping at tool fluency: move learners from recognizing tools through critical evaluation to ethical synthesis and governance-level foresight.
  • Curriculum designers. Assess ethical reasoning and reflective thinking as named outcomes, since in this survey they were the strongest predictors of sustainable and trustworthy AI use.
  • Policymakers. Fund governance and accountability training ahead of more tool training: technical literacy scored highest (M = 3.87) while governance literacy scored lowest (M = 3.21) across all four professional groups.
  • Policymakers. Align actions with the priorities respondents themselves ranked — public awareness on SDG 16 (24.5%), curriculum training on SDG 4 (21.5%) and government policy on SDG 16 (18.8%) — when sequencing literacy investment.
  • Researchers. Use the ASLI and NAI indices with the AIRE taxonomy as a baseline instrument for the cross-country validation the authors call for, rather than building a new measure.

Limitations

  • The sample is 300 respondents from a single national context (Bangladesh); the authors state the findings should be interpreted as indicative rather than globally representative.
  • The design is cross-sectional and captures perceptions at one point in time, so the authors caution that the correlational findings "do not imply causal relationships" — the regression "predictors" describe association only.
  • All domain scores and nexus awareness are self-reported 1–5 Likert means (governance 3.21, technical 3.87), so they measure perceived readiness rather than observed capability.
  • Policymakers are only 15% of the sample (students 40%, educators 25%, professionals 20%), which the authors say limits how far governance-specific conclusions can be generalized; no IRB review was required for the minimal-risk survey.

Connected Concepts

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Citation

Islam, M. M., Morshed, M. N., & Islam, M. S. (2026). Artificial Intelligence Literacy and Sustainable Development: An Ethical Governance and Development Goals Framework. arXiv preprint arXiv:2609.10489.

Metadata

Study designSurvey
LevelAdult Learning, Higher Ed
Intended audiencePolicymakers, Curriculum Designers, Researchers
Research methods and evaluationQuantitative Research
Institutions and policyAI Governance
Equity, ethics, and responsible useEquity, Ethics, Sustainability
Foundations of AI in educationAI in Education, AI Literacy, Critical Thinking, Curriculum Design
Page kindFramework
SourceHistoryEditChat

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