The journey to incorporating AI in education is underway
Based on a global study covering five years of data on tertiary education (conducted in October 2024 by S&P Global Ratings), Singapore, Germany, and the Republic of Korea appear best equipped to support AI education. Our analysis included: the percentage of graduates with science, technology, engineering, and mathematics (STEM) degrees; the number of scholarly publications on AI published in the country; a country's information and communication technology (ICT) infrastructure (expressed as ICT readiness for AI); and whether a country has a national AI strategy in place. We note that Western countries tend to have a wider range of studies, including blended degrees, which likely dilutes their STEM graduate numbers, compared with countries offering fewer study options. That view is supported by evidence that the U.S. and U.K. are world leaders in combined AI and English courses, according to data from the OECD (source: OECD AI, 2024).
AI could enable a more virtuous education ecosystem
AI’s capacity to transform the education industry will be multi-faceted (encompassing teaching, learning, and administrative functions) and applied across schools, universities, academia, and businesses through professional education. The technology could also contribute to improved accessibility, for example, by providing high-quality education to lower-income or disadvantaged students, offering new possibilities to reduce inequality and improve living standards among educationally-underserved sections of society.
The use of machine learning and deep learning techniques in education isn’t new. Adaptive exams (e.g., tests that adjust to a student's capabilities, with correct answers prompting harder questions) and models that automate the reviewing and grading of students’ work have existed for years. However, the rapid evolution of natural language processing in the form of large language models (LLMs), and, more recently, in multi-modal generative AI models (which process information in different formats, such as audio, image, text, and video) brings new opportunities. One opportunity that is already in use is teacher augmentation, a human-centered innovation that provides teachers with expert guidance and consultation from AI tools (see the Appendix for an example of a learning path created with AI and using revised Bloom's Taxonomy, a learning framework developed in the 1950s by educational psychologist Benjamin Bloom). A recent study by Stanford University found that AI assistance for less-skilled tutors could improve students' assessment outcomes by nine percentage points. In that example, tutors received AI-generated questions and explanations to address student mistakes. Stepping into the future, a multi-modal AI tutor/copilot might incorporate information on a student’s academic results, behavior, and learning needs, with the information coming from videos, Internet of Things (IoT) devices that measure quality of sleep and health metrics, and even brain-computer interfaces. Further autonomy could be offered by systems that utilize agentic AI (which offers the capacity to perceive an environment, act autonomously within defined parameters, and learn). With that technology, an AI tutor could act only when a need is identified and develop a personalized learning path based on experience with the student. Whether that sort of AI autonomy is ethical, appropriate, and safe will be a key consideration and challenge for regulators, policymakers, and parents. Regulating AI in education: An evolving challenge
While governments are studying the potential rewards of using AI, they are also grappling with the potential risks of its applications in teaching and learning, including ethical and legal issues. In Europe, the EU AI Act expresses the need to classify certain education-related AI models as high risk because they may determine the professional course of a person’s life or their ability to secure a livelihood. Such AI models are thus deemed to require closer supervision and greater transparency. Examples include AI systems that determine access to educational or training institutions, models that evaluate individual learning outcomes, and those that assess the appropriate level of education for a professional role. In the U.S., the regulatory approach is less prescriptive and not dictated by comprehensive federal AI regulation. However, the White House Office of Science and Technology Policy's "Blueprint for an AI Bill of Rights" notes that enhanced protections and restrictions for sensitive domains such as education and data pertaining to youth are a priority, and that continuous surveillance and monitoring should not be used in education. A U.S. Department of Education report also highlighted concerns about algorithmic bias and resultant discrimination with regard to surveillance and monitoring, admissions processes, and disciplinary interventions. In China, the regulatory approach is proactive and seeks to ensure alignment with government policy and oversight, set out in the "Interim Measures for the Management of Generative Artificial Intelligence Services." Existing regulations provide guardrails for AI education algorithms to be aligned with "socialist values," ensure students’ data privacy, and maintain transparency and fairness, particularly when relating to recommendation algorithms. Additionally, the Ministry of Education actively promotes AI's integration into education systems. More broadly, we observe a trend toward governments incorporating guidance from transnational organizations and standards-setting institutions. While these frameworks provide valuable guidance, we believe there is a growing need for more specific and practical recommendations tailored to the unique needs of educational institutions, which are subject to significant public scrutiny and may be hesitant to adopt or experiment with AI technologies without clear and shared guidelines.
Regulators' concerns are likely to increase as AI gains in autonomy and complexity, including as agentic AI is deployed more widely and develops new capabilities, possibly including the ability to infer people's emotions, which could lead to hidden bias risk. Regulating specific AI risks in education will be challenging, as will navigating those regulations. We consider it likely that standards in different jurisdictions, and notably in Europe, could result in the prohibition of certain AI applications. Education for everyone: How AI can improve accessibility Accessibility refers to equitable access and usability of a service regardless of a person’s abilities, disabilities, or circumstances. In an educational context, AI offers the potential to improve accessibility to the benefit of disabled people, or about 16% of the global population as of 2024. Examples of AI tools in this context include: Image descriptors and book/screen readers for people with vision impairment. Visual AI-based alerts for people with hearing difficulties. Speech therapy support (e.g., Speech Blubs app) for students with speech impairments. AI tools can also help develop social skills and aid in behavioral analysis, including through early diagnosis of issues and beneficial intervention, for example, for autistic students. Equally importantly, AI-driven apps can also adapt output formats to accommodate different levels of accessibility and literacy (e.g., tailoring them to a technical, financial, or basic education audience). That offers the potential to make education more effective across different parts of societies. Finally, AI offers opportunities to enhance access to high-quality and low-cost education in developing economies. That could be through real-time translation of educational resources or via AI tools that can tutor students and thus offset a lack of teachers in some communities. The potential for AI to play a role in reducing the education gap between developed and developing economies is substantial: as of 2023, about 29% of potential graduates in developing countries didn't complete high school, compared with 5% in developed countries. Readying education for labor market change A gap has developed between the fast-evolving demands inherent to development and management of technologies and the typical educational curriculum. To some extent, that is an historical lag, with education still influenced by the remnants of a 20th-century mindset that demanded basic literacy, numeracy, and manual skills. We think that a forward-looking education system should focus on problem solving, communication skills, and technological and financial literacy. Subjects adapted to that skill set will include advanced digital literacy (AI technologies and cybersecurity), creative problem solving (design thinking, intellectual curiosity, metacognitive processes, systems interaction), social and emotional skills (collaborative working), and ethics and responsibility (technology and sustainability). Educational institutions focused on professionals might be expected to adapt quickly to the rapidly evolving environment. But changes to curricula and learning methods across many primary schools, middle schools, and high schools will take time, not least due to the need for new resources and a cultural change. Exceptions to that are likely to come from the private education sector, where institutions often have deeper pockets and thus tend to adapt to change more quickly than the state sector. That flexibility has helped novel private education institutions to fill the gap between more traditional education programs and the growing demand for future skills. We collected and analyzed (using multiple LLM analyses) data from 89 primary and secondary schools, from around the world, that cater to some of those future skills. Our findings suggest that learning methods at 65% of the schools use AI as the backbone of their teaching method, while robotics is core at 13%. Furthermore, about 89% of the schools' primary focus is on critical thinking, and 74% use research-based education methods — both of which we believe will help prepare students for the rapidly evolving labor market. In comparison, schools that remain wedded to traditional practices or struggle to adapt due to a lack of resources risk failing to prepare students for the evolving economy.
AI's disruption of education promises benefits Innovations powered by AI will change economies, society, and the labor markets. New jobs will emerge, some will evolve, and others will disappear. Those shifts will require new skills and a change in the paradigms that dominate education and educational systems. Understanding the role AI could play in driving improvements to education, and the associated risks, will be key to effectively managing the coming change. Ultimately, we think that AI can be a force for positive disruption that could revolutionize education to the benefit of collective well-being, improved accessibility, and reduced inequality. Appendix
Linking AI to Bloom’s Taxonomy In the 1950s, structured thinking and systematic problem solving emerged as a focus of studies aimed at decoding the structure of human intellectual processes. That work became the basis of the origins of artificial intelligence, with mathematician Alan Turing, and of Bloom’s taxonomy, developed by psychologist Benjamin Bloom. Both sought to break down complex concepts into smaller processes — algorithms in the case of AI and intellectual concepts for Bloom. Fast-forward 85 years, and greater computing power and data analysis are proving to be catalysts for algorithms that combine AI and Bloom’s taxonomy. The result is new AI-driven educational tools that support more effective structured learning and problem solving to the benefit of both teachers and students. An example of this, using a revised Bloom's Taxonomy, is provided here. We used generative AI to illustrate a Bloom's Taxonomy approach to teaching a 10-year-old student about geography.
https://www.spglobal.com/en/research-insights/special-reports/ai-and-education
2025/1/29 18:25:00