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1.5 Current State of AI in Education

Take Home message

 

  • Technical understanding and critical thinking: teachers and students need to understand how language models work (training, data handling, predictive nature) and develop practical verification skills, from spotting fabricated citations to fact-checking AI-generated content.
  • Responsible use and ethical stance: a grasp of the legal and ethical landscape (AI Act, GDPR) is needed, along with an approach to AI as a support rather than a substitute for human thinking, grounded in transparency and accountability, plus explicit instruction in prompting as a teachable skill.
  • Equity and new assessment models: equitable access to AI tools needs safeguarding (public funding, open models, minimum age policies), alongside a shift in assessment from grading the final product to evaluating the process and students' ability to critically engage with AI output.

If you remember only one sentence from this page: AI is already present in educational contexts but is not regulated. Structured frameworks are needed to support educators' professional development improving their knowledge and letting them teach with AI and about AI.

Current Uses of AI in Educational Contexts

Nowadays, artificial intelligence (AI) is embedded in everyday educational practice, well beyond pilot projects. Common applications include adaptive learning platforms, intelligent tutoring systems, automated grading, and AI-assisted lesson planning for teachers (OECD, 2026). The most visible shift, however, is the direct use of general-purpose tools by students and teachers alike. According to OECD's TALIS survey, 37% of lower secondary teachers reported using AI in their work in 2024, and 57% said it helps them improve lesson plans — though 72% also expressed concern about students passing off AI-generated work as their own (OECD, 2026). The OECD cautions that better task performance does not always mean genuine learning, warning of a risk of "metacognitive laziness" when cognitive work is offloaded to AI without pedagogical guidance.

Current State of Teaching With AI

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Current State of Teaching About AI

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AI Usage Across Educational Levels

What is missing?

Alongside institutional frameworks and adoption data, it is important to understand how these dynamics play out in practice. A series of workshops was conducted with education stakeholders — including teachers — to gather first-hand perspectives on the current use of AI in teaching. These workshops surfaced a set of concrete needs and concerns expressed by educators, covering practical, pedagogical, and training-related aspects. The following sections outline these needs in detail.

  • Understanding How AI Works. Both students and teachers need to understand how language models are built and trained, and what happens to their data once provided. They also need to grasp the nature of these models as prediction engines rather than databases, which explains their tendency to hallucinate. Teachers need a simple high-level course covering the basics of how LLMs work.
  • Critical Thinking, Questioning and Verifying AI. Students and teachers need to be able to question AI-generated answers and to recognize fabricated information. Practical verification skills are also needed, from checking AI output against official sources to spotting AI-generated images and videos. Students need to shift from following instructions to formulating them, thinking critically about the desired output.
  • Ethics, Data Protection and Regulation. A general understanding of the ethical and legal aspects of AI use is needed, including the EU AI Act and GDPR. Safe and responsible use is essential to prevent illegal practices along with awareness of related issues like bias in teaching materials and the unreliability of AI detectors.
  • Responsible Use. AI needs to be used mindfully as a support, not a replacement, leaving room for students' own thinking. Transparency matters, with students expected to flag which parts are their own, echoing "cyborg writing", where AI extends rather than replaces the writer's thinking. Student ownership and accountability are also needed.
  • Practical Skills. Prompting needs to be treated as a teachable skill, not something acquired independently. Both teachers and students need explicit instruction in writing effective prompts, and teachers need training in prompt engineering for lesson planning, differentiation, and grading. Broader tool familiarity is also needed to match tools to subjects.
  • Equity and Infrastructure. Access to AI is at the centre of growing equity concerns, as wealthier districts can afford better tools, widening competence gaps between schools. Public funding is needed to guarantee equal access regardless of a school's resources, preferring open AI models over corporate ones as part of this effort. A minimum age for AI use also needs to be defined.
  • Assessment. A different approach to assessment is needed, focused on process rather than final product, including students' reflections on AI-generated drafts. AI-integrated assignments are also needed, grading students on their ability to critique, fact-check and edit AI output, raising questions about the human role when teachers themselves rely on AI for grading. Concrete classroom methods are needed too, such as fact-checking AI text, comparing chatbot responses, debating AI use in homework, and incorporating non-digital tools like flip charts.

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