The first of these domains, the integration of AI into educational systems, is no longer speculative. Machine learning techniques now power adaptive learning platforms, intelligent tutoring systems, automated feedback engines, and generative tools that can draft essays, solve problems, and answer questions in natural language. A systematic review published in Discover Education in 2025 catalogued the techniques used for personalized learning in adaptive education, documenting how recommendation algorithms and learner models adjust the pace, sequence, and difficulty of material for individual students. Related work in AI Magazine has described the underlying architecture of adaptive learning technologies, which typically combine statistical models of student knowledge with content repositories and decision engines that select the next best activity. The technical promise is considerable: a well-designed tutoring system can diagnose misconceptions in real time and intervene precisely where a learner struggles, something even the most dedicated teacher cannot do for thirty students simultaneously.

Yet the evidence also shows that personalization is not a solved problem. A 2024 preprint by researchers including Clément, Sauzéon, Roy, and Oudeyer reported improved performance and motivation in intelligent tutoring systems when machine learning recommendations were combined with learner choice, suggesting that autonomy matters as much as algorithmic accuracy. The lesson, the new paper suggests, is that personalization should augment human agency rather than replace it. Systems that hand students a menu of options alongside data-driven recommendations tend to produce better motivational outcomes than systems that silently steer learners along a single optimized path. This finding carries weight for developers and procurement officers alike, because the design choices embedded in commercial platforms often determine whether students experience AI as a tool they control or a system that controls them.

The second domain, learner development and personalization, raises the question of what students actually need to know in a world saturated with intelligent machines. A growing body of scholarship argues that AI literacy must become a foundational competency, on a par with reading, writing, and numeracy. A review in Interactive Learning Environments examined AI literacy education in secondary schools and found a rapidly expanding but still uneven landscape of curricula, teaching materials, and assessment approaches. In May 2025, the OECD and the European Commission jointly launched an AI literacy framework for primary and secondary education, an attempt to define what young learners should understand about how AI systems work, what they can and cannot do, and how they shape the information environment. Complementing this, the AI4AL project has focused on adult learning, recognizing that lifelong learners also need the capacity to evaluate and interact with AI tools in the workplace and in civic life.

Curriculum and educational content form the third domain, and here the pressures are acute. Research published in Education and Information Technologies in 2025 explored the impact of AI on curriculum development in higher education institutions worldwide, documenting how universities are redesigning programs to include machine learning, data ethics, and human-computer interaction, while simultaneously grappling with how generative AI undermines traditional take-home assessments. Some institutions have gone further and created dedicated degrees; the Autonomous University of Barcelona, for example, now offers a bachelor’s degree in artificial intelligence. Beyond specialist programs, scholars have examined how AI can support interdisciplinary learning, with a systemic review in Education and Information Technologies synthesizing the application contexts, roles, and influences of AI across disciplines. Co-design approaches have also emerged: researchers at the Thirty-Eighth AAAI Conference on Artificial Intelligence described how cross-disciplinary high school teachers worked with computer scientists to build AI curricula that fit local classrooms, an approach the new paper presents as a model for content development that is both technically sound and pedagogically grounded.

The fourth domain concerns the role of teachers, arguably the most contested question in the entire debate. The paper argues against narratives of replacement and for a redefinition of professional practice. Teachers bring something that current AI systems do not: expectations. A narrative review by Rubie-Davies and Hattie published in the Journal of the Royal Society of New Zealand synthesized decades of evidence showing that teacher expectations exert a powerful influence on student achievement. An algorithm that ranks students by predicted performance, if absorbed uncritically by teachers, could amplify bias; used reflectively, the same data could help teachers question their own assumptions. The literature also points to creative applications, surveyed in a 2024 book by Urmeneta and Romero, in which AI supports artistic and design-oriented learning, and to responsible AI education, examined by Aler Tubella, Mora-Cantallops, and Nieves in Ethics and Information Technology, which prepares students to reason about the ethical dimensions of the systems they build and use. Teacher training for these new roles remains a bottleneck, and the paper joins a broader chorus in calling for sustained professional development rather than one-off workshops.

Assessment deserves particular attention because generative AI has destabilized the essay, the problem set, and the take-home exam in a matter of months. A 2024 paper in the International Journal of Educational Technology in Higher Education argued that the classroom response must combine AI literacy, prompt engineering, and critical thinking, teaching students to work with generative tools while evaluating their outputs. Chasokela and Hlongwane, writing on assessing higher-order and critical skills in the era of artificial intelligence, explore how examinations can be redesigned to measure judgment, synthesis, and originality rather than reproduction of information that a language model can generate in seconds. The technical challenge is real: detectors of AI-generated text are unreliable, and arms races between generation and detection technologies offer little assurance. The more promising route, the paper suggests, is to change what is assessed and how, embedding tasks in live performances, oral defenses, project portfolios, and processes that make reasoning visible.

The fifth domain, governance and regulation, frames all the others. The European Union’s Regulation 2024/1689, the so-called AI Act, establishes harmonized rules for artificial intelligence across sectors, including requirements relevant to educational applications. UNESCO has issued both a Recommendation on the ethics of artificial intelligence and a practical guidance document for policy-makers on AI and education, urging governments to adopt human-centered approaches and to protect the rights of learners. Schiff’s influential 2022 article in the International Journal of Artificial Intelligence in Education made the case for education for AI, not merely AI for education, arguing that national AI strategies systematically undervalue the role of education and ethics in preparing citizens. Hu and colleagues, surveying AI in higher education, document how policy development lags behind deployment, leaving institutions improvising rules for tools they have already adopted. The new paper calls for collaborative policymaking that includes teachers, students, families, and researchers, not only technology vendors and ministries.

What emerges from the synthesis is a call for systemic thinking. The five domains are interdependent: personalization technologies shape curricula, curricula redefine teacher roles, teacher roles demand new assessments, and all of it requires governance that anticipates rather than reacts. The author argues that ethically grounded approaches must promote equity, ensuring that AI-rich benefits do not accrue only to well-resourced schools while underfunded systems receive automation as a substitute for investment. Lifelong learning is a second pillar, because the pace of technological change means that a single front-loaded education can no longer carry a person through a career. Collaborative policymaking is the third, distributing authority so that decisions about classroom AI are not left to procurement contracts alone. The work was supported by the Ministry of Science and Innovation of Spain, and the author acknowledges the GDEI Research Team of the Chinese Academy of Educational Sciences and feedback from Margarida Romero. As a brief communication, the paper does not present new empirical data; its contribution is a structured map of a fast-moving field and an argument about direction. That argument is likely to resonate widely, because it refuses both utopian and catastrophist framings and insists instead that the outcome depends on choices societies make now about curriculum, pedagogy, and law. In classrooms from Barcelona to Beijing, those choices are already being made, one lesson plan and one procurement decision at a time.

Subject of Research: Integrating artificial intelligence into education systems across personalization, curriculum, teaching, assessment, and governance.

Article Title: Educational Transformation in the Era of Artificial Intelligence

Article References: Manyà, F. (2026). Educational Transformation in the Era of Artificial Intelligence. Frontiers of Digital Education, 3(1), Article 8. https://doi.org/10.1007/s44366-026-0082-2

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0082-2

Keywords: artificial intelligence in education, personalized learning, AI literacy, intelligent tutoring systems, education policy, teacher roles, curriculum development, assessment, ethics, governance, lifelong learning, generative AI