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AI is already part of many teachers’ work, but adoption is uneven and its reported uses are mostly practical: learning about or summarising topics and preparing lessons or activities. OECD TALIS 2024 results, published in 2025, also show substantial training needs and lower reported use for grading and reviewing student performance. These findings describe teachers’ reports—not proof that AI improves learning outcomes.
How widely are teachers using AI?
Across the education systems participating in OECD TALIS 2024, around one in three teachers reported using AI in their work. Rates varied substantially between systems, so the average should not be read as a uniform global adoption rate. TALIS reports teacher responses and does not establish why use differs by system. OECD, Teaching for today’s world: Results from TALIS 2024.
Here, AI is broader than generative chatbots or large language models. The OECD report’s definition also encompasses technologies such as natural-language processing, speech recognition, learning analytics, data mining, image recognition and autonomous agents. TALIS figures therefore should not be treated as chatbot-specific statistics.
What are teachers using AI to do?
Among teachers who said they used AI, the most commonly reported tasks were support activities. The percentages below apply to AI-using teachers, not to all teachers.
| Reported task | Share of AI-using teachers |
|---|---|
| Learn about or summarise a topic | 68% |
| Generate lesson plans or activities | 64% |
| Assess or grade student work | 26% |
| Review data on student participation or performance | 25% |
These are OECD averages across participating education systems from TALIS 2024, published in 2025. The contrast suggests that teachers more often report using AI for preparation and information support than for assessment or analysis of student data. It does not show that schools prohibit higher-stakes uses, or that every teacher uses the same tools or practices. OECD TALIS 2024 results.
What do teachers see as the benefits and risks?
Preparation tasks can make AI useful as a starting point: a teacher may use it to explore a topic, outline an activity or summarise material before checking and adapting the result. That is a description of potential workflow support, not evidence that the tool’s output is accurate, appropriate for a particular class, or educationally effective.
Concerns are part of the same picture. Around seven in ten teachers in the OECD findings believe AI could enable students to misrepresent other people’s work as their own. Teachers also report concerns about bias and privacy or security. These are reported views and concerns, not measured rates of misconduct, biased outputs or data breaches. The OECD cautions that AI’s short- and long-term influence on education remains uncertain.
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In TALIS 2024, 29% of teachers across participating systems identified a high professional-learning need for skills in using AI for teaching and learning, while 38% reported having participated in professional learning focused on AI. The two measures capture different things: a reported high need and reported participation. They do not by themselves show the quality, duration or effectiveness of training. OECD, Developing teacher expertise: Results from TALIS 2024.
A useful preparation plan goes beyond learning prompts or buttons. Teachers need enough technical understanding to judge what a tool does, professional judgment to decide when it belongs in a lesson, and safeguards for human agency, student rights and responsible use. UNESCO’s framework offers a structured reference for building those capacities.
How UNESCO’s framework can guide teacher preparation
UNESCO’s 2024 AI Competency Framework for Teachers organizes 15 competencies across five dimensions, with three progression levels—Acquire, Deepen and Create. It is guidance for developing policy, training programmes and assessment parameters; it is not evidence that teachers have already acquired the competencies.
| Dimension | What it brings into teacher preparation |
|---|---|
| Human-centred mindset | Keep people, teacher rights and human agency central when using AI. |
| Ethics of AI | Consider ethical implications, including risks such as bias and privacy. |
| AI foundations and applications | Build understanding of AI concepts and how applications work. |
| AI pedagogy | Connect AI use to teaching practice rather than treating the tool as the goal. |
| AI for professional learning | Use AI in ways that support teachers’ ongoing professional development. |
The progression levels provide a way to distinguish introductory understanding from deeper practice and the ability to create or shape approaches. UNESCO presents the framework as a global reference, with an emphasis on teacher rights, human agency and sustainability. UNESCO, AI competency framework for teachers.
What the evidence can—and cannot—say about AI in education
TALIS gives a cross-system picture of teachers’ reported use, views and professional learning. It is not a controlled study of whether AI causes better teaching or higher student achievement. The reported use figures show that AI is entering some teachers’ work; they do not establish its effect on learning.
Institutional guidance has also been an early concern, but one UNESCO figure needs careful dating and scope. In a survey conducted 4–19 May 2023, slightly more than 450 institutions responded, and fewer than 10% of the responding schools and universities reported formal guidance on AI. This was a survey of UNESCO-associated networks, not a census or a current global estimate. UNESCO’s 2023 survey report.
For teachers and school leaders, the practical implication is to treat adoption, training and safeguards as connected questions: identify the task, decide what requires human review, and prepare staff to evaluate both outputs and risks. The available findings support that measured approach more strongly than claims that AI has already transformed learning at scale.
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