AI-powered classrooms: Balancing innovation and sustainability in future schools

AI-driven education is often portrayed as a transformative force capable of personalizing learning experiences, automating administrative tasks, and enhancing student engagement. Technologies such as adaptive learning systems, AI tutors, and automated grading tools have the potential to democratize access to quality education. However, these advancements come with hidden costs - particularly in terms of energy consumption, carbon footprint, and environmental sustainability.

As artificial intelligence (AI) and digital technologies become increasingly embedded in education, discussions about the future of schools are intensifying. While AI promises personalized learning, automation, and efficiency, its environmental impact and energy consumption raise critical questions about sustainability.

The study "Future Schools and the Energy Implications of AI in Education: A Review of Scenarios and Method for Engaging Young People in Futures Thinking", authored by Fareed Kaviani, Neil Selwyn, Yolande Strengers, Kari Dahlgren, Bronwyn Cumbo, and Markus Wagner from Monash University and the University of Technology Sydney, explores the intersection of AI-driven education and its energy footprint. Published in Policy Futures in Education (2025), the study examines how future school scenarios address AI’s sustainability challenges and highlights the need to involve young people in shaping the future of education.

The promise and challenges of AI in education

AI-driven education is often portrayed as a transformative force capable of personalizing learning experiences, automating administrative tasks, and enhancing student engagement. Technologies such as adaptive learning systems, AI tutors, and automated grading tools have the potential to democratize access to quality education. However, these advancements come with hidden costs - particularly in terms of energy consumption, carbon footprint, and environmental sustainability.

The study notes that AI requires vast computing power, relying on energy-intensive data centers and cloud infrastructure. In 2022 alone, global data centers consumed an estimated 240–340 terawatt hours (TWh), a number expected to rise as machine learning models become more complex. AI tools such as ChatGPT, Google Bard, and AI-driven virtual learning platforms contribute to this energy demand, raising concerns about their long-term feasibility in the context of global carbon reduction targets.

Despite these concerns, current discussions on AI in education rarely consider its environmental impact. The study found that most "future schools" scenarios focus on technology integration without addressing how schools will manage the growing energy needs of AI-powered classrooms. This gap in discourse highlights the urgency of rethinking educational technology through a sustainability lens, ensuring that AI's benefits do not come at the cost of environmental degradation.

Scenario planning for sustainable future schools

Scenario-building has long been used to envision the schools of the future, helping policymakers, educators, and researchers explore how technological, economic, and societal shifts will impact education. The study analyzed 70 future school scenarios from 18 reports, revealing that while many scenarios emphasize digital transformation, few consider the climate, energy, and environmental implications of AI in schools.

To bridge this gap, the researchers developed scenario cards to help young people engage in futures thinking about AI, sustainability, and education. These cards encourage students to explore questions such as:

• How can AI-powered schools balance innovation with energy efficiency?
• What role can renewable energy play in sustaining digital classrooms?
• How might climate change and extreme weather events disrupt education?

By involving students in scenario development, the study challenges dominant policy and industry narratives, fostering a more inclusive and critical approach to educational futures. The researchers argue that young people - who will be most affected by AI-driven education - should have a voice in shaping sustainable learning environments.

AI, energy, and the digital divide in education

While wealthier nations are rapidly integrating AI into classrooms, the study highlights growing disparities in energy access that may widen the digital divide. Schools in low-income regions often lack reliable electricity and internet connectivity, limiting their ability to adopt AI-powered learning tools. The study cites energy poverty as a major barrier to educational equity, warning that AI’s energy demands could reinforce existing inequalities rather than solve them.

At the same time, schools have the potential to be part of the renewable energy transition. The study points to opportunities for solar panels, battery storage systems, and energy-efficient infrastructure to support AI-driven education sustainably. Countries like Australia, which has over 9,600 school buildings, could harness solar energy to power digital learning tools, reducing reliance on fossil fuels.

However, without policy interventions, the proliferation of AI in education may lead to higher overall energy consumption, negating potential sustainability benefits. The researchers call for stronger regulations, investment in green AI models, and incentives for schools to integrate renewable energy solutions alongside AI adoption.

Rethinking the future of AI in schools

The study concludes with a call for a more holistic approach to AI-driven education, urging policymakers, educators, and technology developers to prioritize both innovation and sustainability. Instead of viewing AI as an isolated advancement, it must be considered within the broader context of climate change, energy consumption, and equitable access to technology.

Future schools must balance technological ambition with environmental responsibility, ensuring that AI enhances education without exacerbating global energy challenges. The study advocates for:

• Green AI research to develop energy-efficient machine learning models.
• Renewable-powered educational infrastructure to support digital classrooms.
• Inclusive policymaking that involves students in shaping sustainable AI futures.

Ultimately, the future of AI in education is not just about what technology can do, but how it can be implemented responsibly. By addressing energy implications alongside AI advancements, we can create schools that are not only smarter but also more sustainable and inclusive.