AI and environmental sustainability in post-16 education: a practical guide

As AI tools become increasingly embedded across teaching, research and estate operations, the AI and environmental sustainability in post-16 education guide urges institutions to move beyond passive adoption and take informed, responsible action. This new practical guidance from EAUC and Jisc aims to bridge the gap between the accelerated development of AI tools and sustainability ambitions across UK post-16 education and research.

The guide is available to read via the Jisc website. EAUC and Jisc will host an online AI and environmental sustainability masterclass on 14 July 2026.

Key highlights

Environmental, social and ethical issues 

While this guidance focuses primarily on environmental impacts, it also recognises the closely linked social sustainability implications associated with AI use. Issues such as equity of access, digital inclusion, workforce impacts, and resource and human rights implications are intrinsically connected to environmental considerations and should not be addressed in isolation. 

Progress and progression 

AI is already widely used across post-16 education and research, sometimes invisibly. From AI-assisted marking and chatbots to energy management systems, these technologies are becoming part of everyday institutional life. 

While progress has been made around the implementation of AI policy, with many colleges and universities now having some form of guidance in place, sustainability considerations can often be missing from the conversation. 

A key issue is a lack of transparency from technology providers. Post-16 institutions are increasingly reliant on global AI services, yet often have little visibility of the energy, water or carbon outputs associated with those tools. This makes it difficult to account for AI’s role in institutional emissions, particularly within complex "scope 3" supply chains. 

Another, more tangible challenge is increasing levels of electronic waste. Before any AI model runs a single query, significant environmental harm has already occurred through processes such as material extraction and hardware manufacturing. Colleges and universities must therefore consider the hardware lifecycle behind the AI services they use, ensuring that sustainability, reuse and responsible end-of-life disposal are factored into procurement, supplier governance and AI policy. 

Balancing risks with opportunities 

Despite these risks, the guidance emphasises that AI can also support sustainability goals when used thoughtfully. 

Practical opportunities include: 

• Optimising campus energy use through AI-enabled building management systems 

• Supporting climate research and environmental modelling 

• Extending equipment lifespans through predictive maintenance 

• Automating carbon accounting and reporting 

• Using digital simulations to replace energy-intensive physical processes 

However, it is important to be aware that claims about AI’s environmental benefits are not always backed by evidence. Institutions are encouraged to critically assess vendor claims and focus on proven, targeted uses. 

Practical steps for institutions and users 

• Audit AI use: Build a clear picture of which AI tools are already in use, including those embedded in existing software platforms 

• Ask suppliers the right questions: Use procurement processes to request information on energy use, carbon emissions and water consumption 

• Educate staff and students: Integrate environmental considerations into AI literacy and training programmes 

• Strengthen governance: Include AI’s environmental impact within institutional risk registers and overall operational strategies 

• Start today: For users, having a clear rationale for the use of generative AI and using well-constructed prompts to limit unnecessary outputs are just a couple of ways in which anyone can instantly limit the environmental impact of AI. 

Real progress, that enables the FE and HE sectors to deliver innovative and impactful research, teaching and learning whilst realising sustainability ambitions, comes when the whole institution is engaged, recognising that everyone has a role in using AI responsibly and sustainably. 

A call for collective action 

Sector-wide collaboration will be essential to drive better transparency, improve reporting standards and influence suppliers when it comes to the sustainable use of AI.