AI and Blockchain Revolutionize Education, but Environmental Concerns Grow

Artificial intelligence (AI) and blockchain technology are transforming traditional educational systems by providing new infrastructure for alternative educational models and expanding access to education globally. These technologies are enhancing the efficiency and effectiveness of educational processes and addressing long-standing challenges in the sector.

AI can personalize learning experiences by adapting to individual student needs, providing real-time feedback, and identifying areas where additional support is required. This personalized approach can significantly improve student outcomes and engagement. Meanwhile, blockchain technology ensures the security and transparency of educational records, making it easier to verify credentials and track academic progress. This decentralized system can also facilitate the recognition of non-traditional learning pathways, such as online courses and micro-credentials, which are becoming increasingly popular.

John von Seggern, an online educator and founder of the Futureproof Music School, an online school that teaches students electronic music production, currently uses an AI assistant to help structure and supplement courses for his students. He plans on rolling out blockchain-based credentials for those who complete the educational courses, providing verifiable proof that they have completed the programs and attained a sufficient understanding of the material.

“Significant momentum exists worldwide to use blockchain technology for issuing, sharing, and verifying educational experiences and qualifications,” according to a report from the Organization for Economic Co-operation and Development (OECD). The authors wrote: “Blockchain technology enables anyone to validate claims about an individual or institution, including their characteristics and qualifications, and to do this instantly and with a very high level of certainty.” This helps eliminate records fraud; facilitates the movement of learners and workers between institutions and geographies; and empowers individuals by giving them increased control over their data.

Blockchain credentials, coupled with AI-powered content, provide borderless verifiability and more affordable classes for students. In January, Open Campus, a decentralized autonomous organization (DAO) that uses blockchain for education applications, launched EDU Chain, its layer-3 blockchain. The EDU Chain will be used to store onchain student credentials and certificates that are tamper-proof, immutable, and verifiable.

Speaking at Token2049 in Dubai, United Arab Emirates (UAE), Binance co-founder CZ said he wanted to teach 1 billion kids through Giggle Academy, a free online platform that provides children’s education. CZ added that generative AI played a central role in crafting the course content for his Giggle Academy project.

However, the adoption of these technologies is not without its challenges. The rapid advancement of AI has raised concerns about its environmental impact, particularly in terms of energy and water consumption. Data centers, which support AI workloads, are expanding at an alarming rate, placing increasing pressure on global resources.

AI, data centers, and cryptocurrency operations consumed approximately 460 terawatt-hours of electricity in 2022, accounting for nearly two percent of global electricity demand. By 2027, AI alone could require between 85 and 134 terawatt-hours annually, an amount equivalent to the total electricity consumption of the Netherlands. This growth in AI infrastructure is already putting pressure on national energy supplies.

The environmental impact of AI extends beyond electricity consumption. Data centers require vast amounts of water for cooling, often drawing from potable sources that could otherwise serve local populations. In 2023, Google reported that 78 percent of its global water withdrawals came from potable sources, heightening fears about water competition in regions where data centers operate.

Some governments have already moved to limit the unchecked expansion of AI infrastructure. For example, Singapore imposed a moratorium on new data centers in 2019, which was subsequently adjusted. The semiconductor industry, which supplies the hardware that powers AI, consumes an estimated 1.2 trillion liters of water annually. The demand for ultrapure water in semiconductor fabrication is particularly concerning, with a single manufacturing facility requiring over 37 million liters daily.

The environmental cost of AI hardware is another growing concern. AI servers and processors require rare earth metals such as silicon, gallium, and tellurium, many of which are sourced from mining operations that disrupt ecosystems and deplete natural resources. The disposal of AI hardware presents yet another challenge, with rising levels of electronic waste linked to AI expansion.

Regulatory efforts and corporate sustainability initiatives are beginning to address these challenges. Governments and regulatory bodies are taking steps toward mitigating AI’s environmental impact. The United Nations Environment Programme advocates AI-specific climate impact reporting, urging companies to disclose energy, water, and material consumption to enhance transparency. Meanwhile, discussions within the G20 and the Organisation for Economic Co-operation and Development are exploring the introduction of carbon pricing for AI workloads to encourage energy-efficient models.

Tech companies are making their pledges to address AI’s sustainability challenges. Google, Microsoft, and Amazon have all committed to making their AI operations carbon-neutral by 2030 or 2040. Hardware manufacturers such as NVIDIA and IBM are developing energy-efficient AI chips designed to reduce power consumption by up to 25-fold over previous generations. Some firms are exploring alternative cooling methods, with Microsoft piloting direct-to-chip and immersion cooling technologies that could significantly reduce AI’s reliance on traditional water-cooled systems.

Despite these commitments, concerns remain about whether the measures being taken are sufficient. Some investigations suggest that major tech companies may be underreporting the emissions of their data centers, raising questions about the transparency of corporate sustainability pledges. More comprehensive data and independent oversight will be necessary to ensure that AI’s environmental impact is accurately assessed and managed.

As AI adoption continues to accelerate, balancing its resource demands with sustainability will be one of the defining challenges of the coming decade. Without intervention, AI’s increasing electricity and water consumption could undermine energy security, intensify water shortages, and accelerate climate change. A coordinated global effort will be required to ensure AI’s future remains sustainable. Stronger regulatory frameworks, increased investment in green computing innovations, and greater corporate accountability will all be necessary to align AI’s expansion with environmental stewardship. The future of AI is at a crossroads. The question is no longer whether AI will continue to grow, but whether its development can proceed without further straining the planet’s resources. The decisions made today will determine whether AI becomes a tool for sustainable progress or an unchecked driver of environmental degradation.