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Sustainability

created 2026-08-28 · updated 2026-09-19

Sustainability — the intersection of two concerns: how AI can be used for sustainability outcomes in education (AI for sustainability, including Education for Sustainable Development and green education), and how to make AI itself sustainable (sustainable AI, reducing the environmental, ethical, and social footprint of AI systems in education). As both users and developers of AI, educational institutions must advance environmental and social goals while ensuring responsible, ethical AI use.

Questions to Consider

  • The page distinguishes two pathways: 'AI for sustainability' (using AI to advance sustainability outcomes) and 'sustainable AI' (reducing AI's own environmental and ethical footprint). Can you think of a way an AI tool could advance one goal while undermining the other?
  • Training and running large language models carries a real carbon and water footprint. When an institution promotes sustainability as a value while deploying energy-intensive AI, what tensions arise — and who should weigh them?
  • Before you read on, how would you define 'sustainable education'? The page treats it as a value-based, human-centered project distinct from using education as an instrument for sustainability — how do those differ?
  • If education is expected to build learners' 'sustainability consciousness,' what role might AI play in that — and could AI integration in the curriculum teach sustainability while its own footprint quietly contradicts the lesson?
  • What would it mean for an educational institution to be genuinely sustainable in its AI use, and which of its decisions (procurement, deployment, teaching) do you think matter most?

Introduction

Sustainability in AIED spans three overlapping framings: sustainable education (a value-based, human-centered educational project), sustainability in education (using education as an instrument for sustainability), and education for sustainable development (ESD, the global policy agenda, especially Sustainable Development Goal 4). AI intersects each of these differently — and the field distinguishes two core pathways: AI for sustainability (AI as a tool to achieve sustainability outcomes) and sustainable AI (reducing AI's own environmental and ethical footprint).

The two-part taxonomy

The knowledge base's coverage, anchored by Daniel et al. (2026), organizes the field into two interconnected yet distinct pathways:

  • AI for sustainability — using AI to advance sustainability outcomes. In education this includes AI for energy management, climate monitoring, green campus programs, and AI-integrated curricula that build learners' sustainability consciousness (e.g. the AI-SEE framework for sustainable engineering education). It is grounded in the global agenda of Education for Sustainable Development.
  • Sustainable AI — reducing the direct environmental and ethical impacts of AI itself. This covers the carbon and water footprint of large language models, energy-efficient and on-premise deployment, and the ethical and governance frameworks needed to ensure AI's use in education is itself responsible and sustainable.

Using AI for sustainability in education

A growing body of work treats AI as a tool for sustainability education and outcomes:

  • AI-integrated curricula that build sustainability consciousness. Liu et al. (2026) propose the AI-SEE framework (intelligence-driven, green-empowered, responsibility-leading, practice-integrated), which integrates AI across the curriculum as a cognitive scaffold and resource for system-level sustainability analysis. In a 144-student engineering case, it enhanced sustainability consciousness and produced behavioral engagement across personal, academic, professional, and social levels, with social diffusion beyond the classroom.
  • AI in green and sustainable education. Talebzadeh (2026) found AI-assisted instructional design under Sustainable Development Pedagogy constraints improved teacher workflows and pedagogical design. Riandi et al. (2026) found that teachers' practical use of AI in science/green energy and their involvement in developing ESD-aligned materials — more than abstract AI knowledge or attitudes — predicted their capacity to integrate AI into green energy education.
  • Sustainability as a value-based project. Alsuhami & Atallah (2026) argue AI's contribution to sustainable education is conditional and governance-mediated: it supports sustainability only when adoption is subordinated to explicit educational values and human-centered purposes, rather than to technologization and commodification. This ties sustainability to Ethics and Critical Pedagogy.

Making AI itself sustainable

The second pathway concerns AI's own footprint in educational settings:

  • Environmental impact of large models. studies of LLM use document the carbon and water footprint of large language models, which is significant given high adoption among university students. This is the direct environmental dimension of sustainable AI.
  • Energy-efficient and on-premise deployment. Shen et al. (2026) demonstrate that AI knowledge-base assistants can run on consumer-grade hardware with open educational resources, reducing the environmental and cost footprint of AI in education — a concrete sustainable-AI design pattern.
  • Ethical and governance frameworks. Sustainable AI is not only environmental: it requires AI Governance and Ethics frameworks ensuring transparency, accountability, human oversight, and equitable access, as Daniel et al. (2026) note is often lacking in current university applications.

Sustainable learning as a pedagogical goal

A related strand frames sustainability not only as an environmental or institutional concern but as a property of learning itself. Zhu et al. (2026) argue AI-assisted learning risks cognitive outsourcing and detachment from authentic contexts, proposing frameworks (E3-HOT) for sustainable learning — learning that persists, transfers, and remains connected to real problems rather than being short-circuited by Cognitive Offloading. This connects sustainability to Learner Agency and Critical Thinking.

Connections to other concepts

Sustainability and AI in education sits at the intersection of Ethics, AI Governance, AI in Education, and the environmental/energy sciences. It draws on Professional Development and Teaching for capacity-building, on Learning Design for pedagogy, and connects to the knowledge base's treatment of Cognitive Offloading and Critical Thinking through the "sustainable learning" lens. Because both pathways are cross-cutting, sustainability is a foundational theme that appears across higher education, K-12, and professional contexts.

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Metadata

LevelHigher Ed, K 12, Teacher Education
Technologies and techniquesGenerative AI
Institutions and policyEducational AI Policy, AI Governance
Equity, ethics, and responsible useEthics, Sustainability
Foundations of AI in educationAI in Education
SourceHistoryEditChat

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