AI is amplifying universities’ contribution to sustainability

In an era marked by escalating environmental crises, widening social inequities and rapid technological advancements, Education for Sustainable Development (ESD) is pivotal to achieving the 17 United Nations Sustainable Development Goals (SDGs).

Universities can advance ESD by embedding sustainability across curricula, research and community engagement – equipping students to lead transformative change while modelling sustainable practices that inspire broader societal transformations.

Emerging technologies like artificial intelligence (AI) offer powerful new tools to elevate these ESD initiatives. This commentary explores how AI can enhance sustainability education, research and institutional operations, while addressing the key ethical considerations universities must navigate to ensure that AI adoption boosts – rather than compromises – sustainability values.



Transforming sustainability education

ESD fosters essential competencies for creating sustainable societies – including systems thinking, anticipatory reasoning, normative understanding, strategic action and collaborative problem-solving. Universities are progressively integrating these competencies into their educational frameworks, preparing students to emerge as tomorrow’s sustainability leaders.

However, sustainability challenges differ significantly across geographic, cultural and socio-economic contexts. Consequently, educational approaches must remain flexible – tackling the interdisciplinary complexity of sustainability while responding to learners’ diverse backgrounds and career aspirations.

For ESD to have real impact, universities should design flexible, culturally sensitive curricula and teaching strategies adapted to distinct student populations, ensuring both relevance and effectiveness.

AI-driven adaptive learning platforms hold particular promise for this application. By analysing comprehensive learner profiles – including prior knowledge, learning preferences, disciplinary backgrounds and regional contexts – these systems can dynamically generate individualised educational pathways specifically designed to develop targeted sustainability competencies.

For example, a student interested in sustainable energy systems might engage with content on renewable technologies and energy policy, while a peer concentrating on social equity could explore sustainability through the lens of social justice and community development. This adapted approach promotes meaningful engagement, deepens understanding, and encourages students to actively contribute to sustainable solutions.

AI-powered intelligent tutoring systems transform traditional pedagogy by actively fostering deeper engagement with ESD principles.

Through personalised feedback and guided inquiry, these systems replicate expert tutoring – continuously monitoring progress, diagnosing misconceptions and offering customised guidance. This approach is especially valuable in ESD, where learners commonly struggle with systems thinking and interdisciplinary integration.

Among AI’s most transformative applications are dynamic simulation environments that model complex systems and future scenarios with striking realism. These tools enable learners to visualise how decisions cascade across environmental, social and economic dimensions. Through immersive experiences, students can safely test decision-making approaches while observing systemic consequences as they unfold.

Through such simulations, students can investigate climate action scenarios – analysing how policy decisions affect environmental health, social equity and economic stability. This experiential learning cultivates systems thinking, deepens socio-emotional understanding, and motivates students to become informed agents of sustainable change.

Accelerating sustainability research

Universities play a crucial role in advancing ESD by building strong knowledge foundations. Their research mission drives innovation, develops new methodologies and addresses multiple SDGs. Today, AI is revolutionising how universities conduct research, providing powerful tools that enhance the creation and sharing of knowledge.

Climate change represents the paramount challenge of the 21st century. SDG 13 highlights the critical need to confront this crisis through a deeper understanding of Earth’s interconnected systems and their anthropogenic influences. AI-enhanced climate models now deliver unmatched predictive accuracy, generating hyper-localised projections that inform tailored adaptation strategies across regions.

This approach aligns with the ESD principle of contextual relevance, enabling learners to connect global issues with local contexts. Advanced visualisation tools make future climate scenarios more tangible, strengthening both cognitive understanding and emotional engagement – key components of transformative learning experiences.

Biodiversity conservation, central to SDGs 14 and 15, is being advanced by AI’s ability to analyse vast amounts of ecological data. Traditional monitoring methods have often been limited by restricted geographical coverage and the need for specialised expertise. Currently, AI-powered systems can process acoustic recordings, camera trap images and satellite data to identify species and assess ecosystems with unparalleled reach and accuracy.

These platforms also integrate indigenous and local knowledge, an approach championed by UNESCO to promote inclusive knowledge creation. By actively engaging local communities, AI-driven solutions support place-based learning, demonstrating how global sustainability principles can be applied to specific ecological contexts.

AI-based tools are transforming food security and sustainable agriculture (SDGs 2 and 12) by bolstering systems thinking, a key competency in UNESCO’s ESD framework. These tools synthesise diverse data, including climate conditions, soil health, agricultural biodiversity and traditional farming practices. Farmers in climate-vulnerable regions benefit from advanced models that combine historical knowledge with future predictions.

For educational purposes, AI-driven visualisations illustrate how agricultural practices, resource management and consumption patterns intersect, helping students understand the connections between food production and broader outcomes, such as water security, biodiversity preservation, climate stability and human health.

Sustainable urban development, as outlined in SDG 11, is significantly advanced by AI’s ability to consolidate and analyse cross-sector urban data – from transportation networks and residential infrastructure to energy distribution and waste management. AI-driven urban models enable both policy-makers and students to visualise in real time the interconnected impacts of policy interventions across various sustainability dimensions.

This transdisciplinary approach to ESD learning prompts students to think beyond individual academic disciplines, helping them develop the strategic skills needed for future sustainability leadership.

Partnerships, as emphasised by SDG 17, are strengthened through AI’s ability to expand international research collaboration. Multilingual Natural Language Processing (NLP) technologies break down language barriers in sustainability research, enriching diverse perspectives – particularly those from the Global South – in global scientific discussions.

Moreover, AI-driven citizen science platforms stimulate global participation in sustainability research, fostering networks for collaborative data collection and analysis. These innovations resonate well with UNESCO’s vision of inclusive knowledge societies, where diverse cultural perspectives contribute equally to addressing sustainability challenges.

Supporting university operations, community engagement

AI enhances university operations and community engagement by enabling the monitoring of sustainability metrics and supporting evidence-based decision-making for systemic transformation. With their multifaceted infrastructures and diverse communities, universities serve as ‘living laboratories’, providing experiential learning opportunities for ESD.

By strategically aligning campus operations with academic programmes, universities achieve dual benefits: demonstrating institutional responsibility while creating authentic opportunities to showcase sustainable practices. AI-powered systems make this integration possible by simultaneously supporting operational efficiency, educational goals and research initiatives.

AI-powered real-time monitoring systems can generate detailed ‘sustainability dashboards’ that track energy use, water consumption, waste generation and carbon emissions across campus buildings and activities with unmatched precision.

When these systems feature student-accessible interfaces, they not only promote data literacy but also advance transparency and accountability – essential practices for cultivating sustainability leadership.

For instance, students across disciplines can access campus sustainability data, applying theoretical knowledge to real-world challenges.

Engineering students might analyse building efficiency patterns, business students could evaluate resource allocation decisions, and environmental science students might track ecosystem impacts – all leveraging the same integrated data systems while approaching them through distinct disciplinary lenses.

Beyond operational efficiencies, AI enables advanced lifecycle analyses for university procurement and investment decisions, transforming these processes into valuable educational resources within business, ethics and sustainability curricula.

NLP can automatically verify vendor sustainability claims against performance metrics, while machine learning algorithms assess the carbon, water and social impacts embedded in global supply chains.

Incorporating AI-driven analyses into case studies allows students to practice key ESD competencies – critically evaluating sustainability claims, balancing complex trade-offs, and making evidence-based decisions.

AI is also transforming university-community engagement into a central educational strategy for sustainability. Cutting-edge stakeholder mapping tools powered by AI can identify under-represented voices in sustainability discussions, while sentiment analysis technologies allow institutions to proactively capture and address community concerns.

Digital twin technologies – virtual replicas of campuses and their surrounding areas – enable collaborative scenario planning, helping stakeholders visualise potential futures and co-design impactful interventions.

These interactive platforms offer unique, problem-based learning experiences, empowering students to collaborate directly with community members on real-world sustainability challenges. This fosters transdisciplinary collaboration, a key element of ESD, and allows students to make meaningful contributions to community-driven sustainability solutions.

Ethical imperatives and institutional responsibilities

As universities increasingly adopt AI technologies to advance ESD, they must navigate significant ethical responsibilities beyond simple technological implementation.

Strategic decisions regarding AI will not only influence ESD learning outcomes but also shape public perceptions of technology and its impacts. This responsibility demands rigorous ethical frameworks that guide AI adoption, ensuring alignment with broader sustainability objectives.

The concept of ‘AI ethics by design’ marks a shift from retrospective ethical considerations to proactive integration. Instead of treating ethics as an afterthought or a compliance requirement, universities must weave ethical principles into both the design and implementation of AI systems for ESD purposes.

This approach calls for a fundamental reorientation of technological development and deployment within academic settings, where fairness, transparency and environmental impact are central priorities, not peripheral considerations.

When ethics are intrinsic to both design and implementation, AI becomes a powerful enabler of sustainability rather than a potential source of unintended consequences.

Effective AI adoption for ESD requires robust governance structures with clear accountability mechanisms. This cannot be addressed in isolation; governance must reflect the broader sustainability commitments of universities. To be successful, it should involve interdisciplinary expertise from fields such as computer science, ethics, sustainability studies and educational theory.

This approach ensures that AI systems are evaluated not only for technical performance but also for their overall contribution to sustainable development. Without such comprehensive oversight, institutions risk unintentionally undermining their sustainability goals – such as through hidden environmental costs, algorithmic bias, or the exclusion of certain groups of learners.

The emergence of self-hosted, locally deployed language models presents new opportunities for institutional autonomy and ethical alignment. Smaller, yet increasingly powerful models, such as DeepSeek or Meta’s Llama, offer viable alternatives to proprietary, cloud-based systems that often obscure data governance and environmental impact.

By hosting models on their own servers, universities gain greater control over technology implementation and monitoring. This reduces reliance on remote data centres with large carbon footprints while enhancing data sovereignty by keeping sensitive information within institutional boundaries. Additionally, it can improve accessibility in regions with limited connectivity or resources.

In addition to these advantages, on-premises AI models allow universities to customise applications to their specific ESD goals. Full oversight of model architecture and training data enables institutions to align AI implementations closely with local contexts and proactively address biases or prevent misuse.

Unlike generic cloud-based solutions, this approach directly responds to the nuanced needs of ESD across diverse educational settings. Moreover, as these smaller AI models become more capable and resource-efficient, they pave the way for more equitable AI deployment.

A crucial step in the responsible use of AI for ESD is implementing certification frameworks. These frameworks should establish clear criteria for evaluating AI systems on both technical and ethical grounds, with a focus on environmental impact, inclusivity and learning effectiveness.

Reviews should be transparent and conducted regularly, given the fast-paced advancements in AI technologies and evolving sustainability challenges. By adhering to these certification standards, universities demonstrate their commitment to responsible innovation and provide governance models that can serve as benchmarks for society at large.

Finally, ensuring equity in AI-enhanced ESD requires attention to global disparities in computational resources, technical expertise and digital infrastructure. Without efforts toward digital inclusion, AI could widen the sustainability capacity gap between well-resourced and under-resourced institutions.

Universities with technological advantages must commit to open-source approaches, capacity-building partnerships, and equitable data-sharing to ensure AI benefits sustainability education for all.

Smart tech with responsible stewardship

Integrating AI into ESD offers universities a unique opportunity to redefine their role in shaping a sustainable future. By embracing AI, universities can strengthen their missions and make a significant contribution to global sustainability goals, providing a more impactful approach to educating future generations about sustainability challenges and solutions.

Realising this potential requires more than just adopting technology – it demands a bold vision and careful deliberation. Universities must balance innovation with ethical considerations, institutional responsibilities and societal impacts. Through transformative applications, they can drive a sustainable future while remaining true to the values of equity, transparency and responsibility.

As AI continues to progress, its ability to amplify universities’ contributions to global sustainability efforts is unprecedented. A thoughtful and strategic approach to AI integration will not only improve educational outcomes but also equip students with the skills and knowledge necessary to lead in a transformed, sustainable world.

Dr Tianchong Wang is a lecturer in STEM in innovative education futures, in the College of Education, Psychology and Social Work at Flinders University in Australia. Dr Libing Wang is the former senior programme specialist in higher education and chief of education at the UNESCO Regional Office in Bangkok, Thailand. He currently serves as the chief of section of health and education at UNESCO Headquarters in Paris, France.