Sustainable Education in the Age of Artificial Intelligence and Digitalization: A Value-Critical Approach

Synthesis: Alsuhaymi and Atallah (2026) offer a qualitative, value-critical analysis of whether — and under what conditions — AI contributes to sustainable education in the age of digitalization. They argue AI does not exert a uniform, inherently positive or negative influence on educational sustainability; its effects are conditional and governance-mediated. Distinguishing sustainable education (a value-based, human-centered project), sustainability in education, and education for sustainable development (ESD), the authors show that AI contributes to sustainable education only when its adoption is subordinated to explicit educational values and human-centered purposes — and undermines it when technologization and commodification are allowed to erode those values.

Key Findings

1. AI's effect on educational sustainability is conditional, not automatic. Whether AI supports or erodes sustainable education depends on how its adoption is governed and to what values it is subordinated — not on the technology itself.
2. Three distinct framings must be distinguished. Sustainable education (human-centered, value-based), sustainability in education (treating education as an instrument for sustainability), and education for sustainable development (ESD, the policy-oriented global agenda) are different projects that AI serves differently.
3. Digitalization brings dual pressures. The same developments that enable personalization and efficiency also accelerate technologization and commodification, threatening the erosion of educational values and human-centered purposes.
4. Value-critical analysis is the corrective lens. A value-critical approach surfaces the normative commitments embedded in AI-mediated education, arguing that sustainability must be reclaimed as a value-based project rather than reduced to technical efficiency.

Relevance to AI and sustainability in education

This paper anchors the sustainable AI in education concept page's normative dimension: it frames sustainability not as a technical or environmental metric but as a value-based, human-centered educational project. It connects directly to Ethics in AI education, to AI Governance (the conditions under which AI use is responsibly governed), and to the knowledge base's treatment of AI's critical and philosophical dimensions.

What this means for practice

• Institutions. Subordinate every AI adoption decision to explicit educational values and human-centered purposes; the paper's central claim is that AI's contribution to sustainable education is conditional and governance-mediated, not automatic.
• Administrators. Keep three framings distinct in strategy documents — sustainable education (value-based, human-centered), sustainability in education (education as an instrument for sustainability), and education for sustainable development — because AI serves each of them differently.
• Policymakers. Design regulation against the dual pressures of digitalization: the same developments that enable personalization and efficiency accelerate the technologization and commodification that erode educational values.
• Administrators. Preserve reflective pedagogy, critical engagement with AI-generated outputs, and meaningful human relationships as the core of educational processes rather than treating them as residual to efficiency gains.
• Policymakers. Make sustainability a stated educational value in policy rather than a technical-efficiency metric, since the paper argues governance conditions — not the technology — determine whether AI supports or undermines sustainability.

Limitations

• This is a qualitative, value-critical conceptual analysis with no primary data: it cannot measure AI's effects on sustainability in any particular institution or national system, so the conditional model is an interpretive framework rather than a tested explanation.
• The argument rests on secondary sources — peer-reviewed literature and policy-oriented normative documents — and the authors state that it does not adopt a systematic literature review or meta-analytic design; database, search-term, and inclusion descriptions serve a scoping function, not an analytical protocol.
• The analysis is positioned at a macro level of depth and scope, addressing normative and governance questions, so it cannot resolve institution-level or classroom-level variation in how AI is adopted.
• The authors identify culturally and religiously grounded contexts, where epistemic authority, moral formation, and technological mediation intersect, as unexamined; no empirical case analysis of enacted governance is included.

Citation

Alsuhami, A. O., & Atallah, F. A. (2026). Sustainable Education in the Age of Artificial Intelligence and Digitalization: A Value-Critical Approach. Sustainability, 18(3), 1257.