KnowledgeInSight
AI Literacy
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Module 2 · Lesson 2

Work and Power: AI in schools and universities

You'll look at how widely students and teachers now use AI, how institutions have responded, and what the evidence says about learning. You'll be able to compare the main positions and say what each relies on.

What you will be able to do

  • Compare positions on AI in education and the evidence on learning that each relies on.

0% of this lesson · 9 items · 1h 3m total · 48m without the optional journal

Contents of this lesson9 items
  1. ReadingNearly Every Student Uses It: The Policy Question Schools Now Face3 min
  2. ReadingStudent and Teacher Use of AI: What Two National Surveys Report4 min
  3. ReadingInstitutional Responses: Guidance from UNESCO and the US Department of Education4 min
  4. ReadingPerformance Versus Learning: The OECD's 2026 Review of the Evidence4 min
  5. ReadingThree Positions on AI in Education: Tutor for Every Student, Threat to Learning, and Assessment Reform4 min
  6. Guided ReadingGuided Close Reading: UNESCO's Guidance on Age Limits and a Human-Centered Approach7 min
  7. Guided ConversationSet a Policy for One Assignment12 min
  8. Journal · optionalThe Position You'd Defend to a School Board15 min
  9. Knowledge CheckAI in schools and universities10 min

Reading 3 min

Nearly Every Student Uses It: The Policy Question Schools Now Face

This content reflects the field as of October 2026.

The public argument about AI in education often starts from the question of whether students should be allowed to use it. Two national surveys suggest that students settled the matter themselves some time ago.

The Higher Education Policy Institute, a research organization in the United Kingdom, has surveyed undergraduates about AI each year since 2024. Its 2026 survey, carried out in December 2025, found that 95 percent of students reported using AI in at least one way, and 94 percent said they used generative AI to help with work that counts toward their grades (Stephenson and Armstrong 2026). The report's own summary is that use "is now almost universal."

In the United States, the research organization RAND surveyed school students, teachers, and school leaders in 2025. It found that 54 percent of students used AI for school. So did 53 percent of teachers of English, math, and science (Doss et al. 2025).

The rules have moved more slowly. In the RAND surveys, 45 percent of principals reported that their school or district had policies or guidance on AI. Over 80 percent of students said their teachers had not explicitly taught them how to use AI for schoolwork (Doss et al. 2025). In the UK survey, 36 percent of students felt encouraged by their institution to use AI, and 38 percent said their institution provided AI tools (Stephenson and Armstrong 2026).

The gap leaves students guessing. Half of the students in the RAND surveys said they worried about being falsely accused of using AI to cheat (Doss et al. 2025). A student who doesn't know where the line is can cross it by accident, or can be accused of crossing it when they didn't.

These findings change the question worth asking. "Should students use AI?" assumes that a school can decide whether use happens. The surveys indicate that it happens with or without permission. The questions that remain open are about conditions:

  • for which tasks AI use helps a student learn, and for which it replaces the learning
  • what a student has to disclose about the help they got
  • what a school assesses, if a chatbot can produce the essay or solve the problem set
  • who gets access to good tools, and who is left with none

Each of these has more than one defensible answer. Some educators see AI mainly as a tutor that every student could have. Others see it mainly as a shortcut that produces finished work without understanding. A third group argues that the real issue is assessment, and that tasks a chatbot can complete need to change.

The surveys can't decide among those views. They record what students and teachers say they do, and they don't measure what anyone learned. They do establish the starting point for any policy: the students it applies to are already using the tools.

References

  • Doss, Christopher Joseph, Robert Bozick, Heather L. Schwartz, and 5 others. 2025. AI Use in Schools Is Quickly Increasing but Guidance Lags Behind: Findings from the RAND Survey Panels. RR-A4180-1. Santa Monica, CA: RAND, September 30, 2025.
  • Stephenson, Rose, and Charlotte Armstrong. 2026. Student Generative Artificial Intelligence Survey 2026. HEPI Report 199. Oxford: Higher Education Policy Institute, March 12, 2026.

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Reading 4 min

Student and Teacher Use of AI: What Two National Surveys Report

This content reflects the field as of October 2026.

Introduction

Claims that "everyone" in education now uses AI are usually based on surveys. Surveys are good evidence for some things and no evidence for others, and the two most cited ones differ in ways that matter.

This reading reports what a UK survey and a US survey found, how they differ, and what a survey can and can't show.

The UK Undergraduate Survey

A survey is a study that asks a sample of people the same questions and reports the share giving each answer. The Higher Education Policy Institute, a UK research organization, surveyed 1,054 full-time undergraduates in December 2025 through a polling company (Stephenson and Armstrong 2026).

Its headline result concerns adoption: the share of a group that has started using a tool or practice. Ninety-five percent of students reported using AI in at least one way. Ninety-four percent said they used generative AI to help with assessed work, meaning work a student submits that counts toward a grade.

Most of that help stops short of handing in what the AI wrote. The share of students who said they include AI-generated text directly in assessed work was 12 percent. That share has risen each year, from 3 percent in 2024 and 8 percent in 2025.

Students' views of the effect were mixed. Almost half, 49 percent, said AI had improved their experience as students, mainly by saving time and helping them understand material. A minority said it had made their experience worse, and they cited fairness, loss of skills, and isolation. Nearly two-thirds, 65 percent, said assessment had changed significantly in response to AI. The report also notes anxiety among some students about false accusations of misconduct (Stephenson and Armstrong 2026).

The US School Surveys

RAND, a US research organization, drew on its standing survey panels of students, parents, teachers, principals, and district leaders. The panels are designed to represent the country's schools from kindergarten through high school (Doss et al. 2025).

In 2025, 54 percent of students and 53 percent of teachers of English, math, and science said they used AI for school. More high school students than middle school students reported using it.

The surveys found worry alongside use. Half of the students said they were worried about being falsely accused of using AI to cheat. Asked whether greater use of AI will harm students' critical-thinking skills, 61 percent of parents and 55 percent of high school students agreed. Among district leaders, 22 percent agreed (Doss et al. 2025). The people running school systems were far less worried than the families they serve.

How the Two Surveys Differ

UK surveyUS surveys
Who was asked1,054 full-time undergraduatesStudents, parents, teachers, principals, and district leaders
LevelUniversityKindergarten through high school
WhenDecember 20252025
Share of students using AI95 percent, in at least one way54 percent, for school
Who ran itA higher education policy instituteA research organization, through its survey panels

The two figures for student use can't be compared directly. The students differ in age, the countries differ, and the questions differ. "In at least one way" is a broader question than "for school." The gap between 95 and 54 percent partly reflects those differences.

What Surveys Can and Can't Show

Both surveys rest on self-report: what people say about their own behavior or views, which may differ from what they actually do. A student asked whether they paste AI text into graded work has a reason to say no. A figure like 12 percent is better read as a floor than as an exact count.

Surveys can show how common a behavior is, how attitudes differ between groups, and how both change from year to year when the same questions are repeated. The rise from 3 to 12 percent is informative for that reason, even if each year's figure is understated.

Surveys can't show whether AI helped anyone learn. When 49 percent of students say AI improved their experience, that is a report of how they feel about it. A student can feel helped by a tool that did their thinking for them. Learning has to be measured by testing what students can do, and neither survey did that.

Conclusion

As of October 2026, surveys show that AI use is nearly universal among UK undergraduates and reported by about half of US school students and teachers, and that it is rising. They also show mixed views of its effect and widespread worry about false accusations. All of this is self-reported behavior and opinion, and none of it measures learning.

Key Terms

  • Survey: A study that asks a sample of people the same questions and reports the share giving each answer.
  • Adoption: The share of a group that has started using a tool or practice.
  • Assessed work: Work a student submits that counts toward a grade.
  • Self-report: What people say about their own behavior or views, which may differ from what they actually do.

References

  • Doss, Christopher Joseph, Robert Bozick, Heather L. Schwartz, and 5 others. 2025. AI Use in Schools Is Quickly Increasing but Guidance Lags Behind: Findings from the RAND Survey Panels. RR-A4180-1. Santa Monica, CA: RAND, September 30, 2025.
  • Stephenson, Rose, and Charlotte Armstrong. 2026. Student Generative Artificial Intelligence Survey 2026. HEPI Report 199. Oxford: Higher Education Policy Institute, March 12, 2026.

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Reading 4 min

Institutional Responses: Guidance from UNESCO and the US Department of Education

This content reflects the field as of October 2026.

Introduction

Within months of AI chatbots reaching the public, schools were asking what to do about them. Two public bodies published answers in 2023, and their documents still shape how school systems talk about the subject.

This reading covers what the two documents recommend, how schools have responded in practice, and how far the guidance has reached. Both documents date from 2023. National policies have changed since then, and this reading doesn't survey them.

UNESCO's Guidance

UNESCO is the United Nations agency for education, science, and culture. In September 2023 it published guidance on generative AI in education and research, written mainly for governments (Miao and Holmes 2023).

The guidance is built on a human-centered approach: the principle that AI should serve the development of human abilities, with people keeping control over decisions. In UNESCO's words, AI "should be at the service of the development of human capabilities" (Miao and Holmes 2023, sec. 3.1).

It lists seven things for government regulators to address. Two have drawn the most attention.

  • Data privacy. The guidance points out that using these tools "almost always involves users sharing their data" with the company that provides them, and it calls for laws protecting users' personal information.
  • An age limit. An age limit is a minimum age set for using a product or service. The guidance says countries should consider the right age threshold for "independent conversations" with generative AI platforms, and adds, "The minimum threshold should be 13 years of age" (Miao and Holmes 2023, sec. 3.3.1).

UNESCO has no power to enforce any of this. Its guidance is advice that member countries can adopt, adapt, or ignore.

The US Department of Education's Report

In May 2023 the Office of Educational Technology at the US Department of Education published a report on AI in teaching and learning. It makes seven recommendations (US Department of Education 2023).

The first is "Emphasize Humans in the Loop." The report explains that people should remain part of the process of noticing patterns in an educational system and deciding what they mean, and that teachers should stay in charge of major decisions about instruction. The other six cover aligning AI with a shared vision for education, designing with modern learning principles, strengthening trust, involving educators, focusing research on context and safety, and developing guidelines specific to education.

Like UNESCO's document, the report recommends and doesn't require. In the United States, most decisions about schools are made by states and local districts.

What Schools Did

Schools and universities didn't wait for guidance, and their responses have taken four broad forms. An institutional policy is the set of rules a school, college, or university sets for its own students and staff.

ResponseWhat it meansMain difficulty
RestrictAI tools are blocked or banned for schoolworkStudents use them anyway outside school systems
Permit with disclosureUse is allowed if the student says how they used itDepends on honest reporting
IntegrateTeachers build AI use into lessons and teach it as a skillNeeds teacher training and suitable tools
Redesign assessmentGraded tasks are changed so they still show what the student learnedTakes time and staff effort

Assessment redesign means changing what students are asked to do for a grade so that the task still shows what they have learned. Examples include oral examinations, writing done in class, and assignments that ask students to show their working.

Many institutions moved through these in order: early bans, then general guidance, then rules set assignment by assignment. That sequence is a broad description, and individual schools vary widely.

How Far the Guidance Has Reached

RAND, a US research organization, surveyed schools in 2025 and titled its report with the finding: use is increasing quickly and guidance lags behind. Forty-five percent of principals said their school or district had policies or guidance on AI. Thirty-four percent of teachers reported a policy on AI and academic integrity. Thirty-five percent of district leaders said they provided students with any training on AI (Doss et al. 2025).

The 2023 documents recommended that people stay in control and that students be protected. Two years later, more than half of US principals reported no policy at all through which those recommendations could operate.

Conclusion

UNESCO and the US Department of Education both recommended, in 2023, that AI in education remain under human direction, with safeguards for privacy and for younger students. Neither document binds any school. As of October 2026, schools have applied a mix of restriction, disclosure rules, integration, and assessment redesign, and survey evidence from the United States indicates that formal policy still trails behind use.

Key Terms

  • Human-centered approach: The principle that AI should serve the development of human abilities, with people keeping control over decisions.
  • Age limit: A minimum age set for using a product or service.
  • Institutional policy: The set of rules a school, college, or university sets for its own students and staff.
  • Assessment redesign: Changing what students are asked to do for a grade so that the task still shows what they have learned.

References

  • Doss, Christopher Joseph, Robert Bozick, Heather L. Schwartz, and 5 others. 2025. AI Use in Schools Is Quickly Increasing but Guidance Lags Behind: Findings from the RAND Survey Panels. RR-A4180-1. Santa Monica, CA: RAND, September 30, 2025.
  • Miao, Fengchun, and Wayne Holmes. 2023. Guidance for Generative AI in Education and Research. Paris: UNESCO, September 7, 2023.
  • US Department of Education, Office of Educational Technology. 2023. Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. Washington, DC, May 2023.

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Reading 4 min

Performance Versus Learning: The OECD's 2026 Review of the Evidence

This content reflects the field as of October 2026.

Introduction

A student who uses a chatbot on a homework problem usually gets the problem right. Whether that student can solve the next problem alone is a different question, and experiments have begun to answer it.

This reading explains the difference between performance and learning, describes two experiments that came out in opposite directions, and reports what an international review concluded from them.

Two Things a Score Can Mean

Performance is how well a student does on a task at the time, with whatever help is available. Learning is a lasting change in what a student knows or can do without help.

The two usually rise together, which is why teachers use homework scores as a sign of learning. AI can separate them. A student with a chatbot can turn in excellent work and retain nothing, because the chatbot did the part of the task that produces learning.

The OECD is an organization of mostly high-income countries that compares their education systems. Its 2026 report on digital education reviewed the research on generative AI. It warns that overreliance on tools that give direct answers can reduce students' engagement, "improving task performance without corresponding learning gains" (OECD 2026, executive summary).

The report ties this to metacognition: thinking about one's own thinking, such as checking whether you understand something and deciding what to do when you don't. The effort of getting stuck and working out why is part of how understanding forms. When a tool removes that effort, the report says, this kind of engagement drops.

The Experiment That Found Harm

The report leans on a field experiment with nearly a thousand high school mathematics students in Türkiye. A field experiment tests something in a real setting, here ordinary classrooms, with students assigned to different conditions (Bastani et al. 2025).

During practice sessions, some students had a standard chatbot built on a commercial language model. Others had a tutoring version of the same model, set up to give hints and withhold full answers. A third group practiced with no AI. All students then took an exam with no AI.

GroupPractice scoresLater exam, without AI
Standard chatbot48 percent higher than students with no AI17 percent lower than students with no AI
Tutoring version127 percent higher than students with no AIClose to students with no AI

The standard chatbot raised performance and lowered learning. Students who used it did worse on the exam than students who never had access. The authors' explanation is that students used it as a crutch during practice (Bastani et al. 2025).

The tutoring version avoided most of the harm. It didn't produce better exam results than studying without AI.

The Experiment That Found Benefit

A second trial points the other way. Researchers at Harvard University built an AI tutor for a large introductory physics course and tested it against a well-regarded teaching method. In the comparison class, students worked through problems in groups with an instructor present, an approach called active learning. The trial included 194 students, and each student experienced both conditions on different topics (Kestin et al. 2025).

Students learned more with the AI tutor, by roughly double on the researchers' measure, and they spent less time. The median time with the tutor was 49 minutes, against a 60-minute class.

The tutor's design was the point of the study. Tutoring design is the way an AI tool is set up to teach, for example by giving hints and asking questions in place of answers. The researchers wrote the correct step-by-step solutions into the tutor's instructions, had it guide students through one question at a time, and followed established teaching practice. The authors designed the tutor they tested, so they have a stake in its success, and they declare no financial conflict.

What the Review Concludes, and Its Limits

The OECD report reads these studies together. Its conclusion is that systems combining generative AI with explicit teaching methods "show more promise than general-purpose chatbots" (OECD 2026, executive summary). On this reading, what a tool is designed to do and what task the student is given matter more than whether students have access.

The evidence base is thin. Each study covers one subject at one level: high school mathematics in one, university physics in the other. Each ran for weeks, not years. The physics trial took place at a highly selective university with a tutor its own instructors built, and its authors note that it tested the early stage of learning new material, not complex reasoning. Whether either result holds for writing, for younger children, or over a whole school career hasn't been tested.

Conclusion

Experiments show that AI can raise students' performance on a task while lowering what they learn, and that a tool designed to tutor can avoid that harm or produce gains. An international review in 2026 concluded that design and task matter more than access. As of October 2026 this rests on a small number of short studies in a few subjects.

Key Terms

  • Performance: How well a student does on a task at the time, with whatever help is available.
  • Learning: A lasting change in what a student knows or can do without help.
  • Metacognition: Thinking about one's own thinking, such as checking whether you understand something and deciding what to do when you don't.
  • Tutoring design: The way an AI tool is set up to teach, for example by giving hints and asking questions in place of answers.

References

  • Bastani, Hamsa, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2025. "Generative AI Without Guardrails Can Harm Learning: Evidence from High School Mathematics." Proceedings of the National Academy of Sciences 122 (26): e2422633122.
  • Kestin, Greg, Kelly Miller, Anna Klales, Timothy Milbourne, and Gregorio Ponti. 2025. "AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting." Scientific Reports 15: 17458.
  • OECD. 2026. OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education. Paris: OECD Publishing, January 19, 2026.

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Reading 4 min

Three Positions on AI in Education: Tutor for Every Student, Threat to Learning, and Assessment Reform

Introduction

Arguments about AI in schools often sound like a dispute between people who are for it and people who are against it. The debate has at least three sides, and they disagree about what the main issue is.

This reading sets out the three positions as their advocates would state them, what each needs to be true, and where the evidence is stronger or weaker. The question is contested.

A Tutor for Every Student

Tutoring is teaching one student at a time, with explanations and feedback fitted to that student. Educators have long regarded it as one of the most effective ways to teach, and one of the most expensive. Most students never get it.

The first position holds that AI can change that, because a well-designed AI tutor is available at any hour and adjusts to each student. Advocates point to a randomized trial in a university physics course, in which students using a purpose-built AI tutor learned more in less time than students in a class taught with active-learning methods. Its authors conclude that the result presents "a compelling case for its broad adoption" (Kestin et al. 2025, abstract).

What this position needs to be true: tools must be designed for teaching, and students must use them as designed. The trial tested a tutor its authors built for one course, not a general chatbot.

A Threat to Learning

The second position holds that in practice AI lets students produce work without doing the thinking that the work was meant to exercise. Advocates point to a field experiment in high school mathematics. Students given an ordinary chatbot during practice scored higher while they had it and lower on a later exam than students who never had it (Bastani et al. 2025).

They also point to students' own reports. In a 2026 UK survey, a growing minority of undergraduates said they place AI-generated text directly into graded work (Stephenson and Armstrong 2026). This raises a question of academic integrity: the expectation that work submitted for credit is the student's own and that any help is acknowledged. UNESCO's 2023 guidance to governments shares the underlying worry. It asks education systems to prevent uses of AI that would "deprive learners of opportunities to develop cognitive abilities" (Miao and Holmes 2023, sec. 4.2).

What this position needs to be true: most real use must resemble the ordinary chatbot in the experiment and not the careful tutor.

Assessment Reform

The third position treats the first two as arguments about the wrong thing. An assessment is any task used to judge what a student has learned. If a chatbot can complete an assessment, the argument runs, then the assessment no longer shows what the student knows, and the task should change. Banning the tool leaves a broken measurement in place.

Advocates propose tasks that reveal a student's own thinking, such as oral examinations, supervised writing, and work that documents its process. In the 2026 UK survey, 65 percent of undergraduates said assessment at their university had already changed significantly in response to AI (Stephenson and Armstrong 2026).

What this position needs to be true: institutions must have the staff time, training, and money to redesign what they assess.

The Three Compared

PositionMain claimEvidence it citesWhat it depends on
Tutor for every studentWell-designed AI can teach each student individuallyA trial of a purpose-built tutor in university physicsTools built for teaching, used as intended
Threat to learningOrdinary use replaces the student's own thinkingAn experiment showing lower exam scores after chatbot use; survey reports of submitted AI textHow students use tools when unguided
Assessment reformTasks AI can complete should be replacedReports that assessment is already changingInstitutions' capacity to redesign

The positions overlap. A person can hold that purpose-built tutors help, that ordinary chatbots harm, and that assessment has to change. A 2026 review by the OECD, an organization of mostly high-income countries, came close to that combination: tools designed around teaching methods show more promise than general-purpose chatbots (OECD 2026).

One concern cuts across all three. Equity of access is whether all students, whatever their income or school, can use a tool on similar terms. If good tutoring tools cost money, the benefits and the harms could fall on different students.

Where the Evidence Is Stronger

The best-supported finding is narrow: in the one large experiment that tested it, an AI tool that hands over answers during practice lowered students' later unassisted performance. The finding that a designed tutor can beat good classroom teaching rests on one course at one university.

What happens at scale and over years is unsettled. No study has followed students through several years of schooling with AI, and none has compared whole school systems that took different approaches.

Conclusion

The tutor position, the threat position, and the assessment-reform position each rest on real findings and each depend on conditions that haven't been shown to hold widely. The evidence is strongest that unguided answer-giving harms later performance, and it is thin on long-term and system-wide effects. Many educators hold parts of all three positions at once.

Key Terms

  • Tutoring: Teaching one student at a time, with explanations and feedback fitted to that student.
  • Academic integrity: The expectation that work submitted for credit is the student's own and that any help is acknowledged.
  • Assessment: Any task used to judge what a student has learned.
  • Equity of access: Whether all students, whatever their income or school, can use a tool on similar terms.

References

  • Bastani, Hamsa, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2025. "Generative AI Without Guardrails Can Harm Learning: Evidence from High School Mathematics." Proceedings of the National Academy of Sciences 122 (26): e2422633122.
  • Kestin, Greg, Kelly Miller, Anna Klales, Timothy Milbourne, and Gregorio Ponti. 2025. "AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting." Scientific Reports 15: 17458.
  • Miao, Fengchun, and Wayne Holmes. 2023. Guidance for Generative AI in Education and Research. Paris: UNESCO, September 7, 2023.
  • OECD. 2026. OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education. Paris: OECD Publishing, January 19, 2026.
  • Stephenson, Rose, and Charlotte Armstrong. 2026. Student Generative Artificial Intelligence Survey 2026. HEPI Report 199. Oxford: Higher Education Policy Institute, March 12, 2026.

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Guided Reading 7 min

Guided Close Reading: UNESCO's Guidance on Age Limits and a Human-Centered Approach

Introduction

News coverage of UNESCO's 2023 guidance on AI in education often reduced it to one line: UNESCO says children under 13 shouldn't use AI chatbots. The passage that line comes from is a single long paragraph, and it says something more limited and more careful.

This reading goes through that paragraph, the recommendations around it, and what it leaves for others to decide.

Locating the Passage

The document is Guidance for Generative AI in Education and Research, by Fengchun Miao and Wayne Holmes, published by UNESCO in September 2023. UNESCO is the United Nations agency for education, science, and culture. The document is free as a PDF from the UK National Commission for UNESCO, listed in the References.

The passage is in section 3, "Regulating the use of generative AI in education." Section 3.3 sorts its advice by who should act: government regulators, companies that provide AI tools, institutions such as schools, and individual users. Subsection 3.3.1, "Governmental regulatory agencies," lists seven "key elements." The age limit is the sixth. It begins with the words "Definition and enforcement of age limit for the use of GenAI." "GenAI" is the document's abbreviation for generative AI.

This reading cites passages by section number. The document uses British spelling, so quotations have "human-centred" and "wellbeing." All quotations are from this document (Miao and Holmes 2023).

Walking Through the Passage

Step 1: Read the recommendation and its exact scope

The recommendation comes near the end of the paragraph, in two sentences. The first says the spread of chatbots demands "that countries carefully consider – and publicly deliberate – the appropriate age threshold for independent conversations with GenAI platforms." The second reads, in full: "The minimum threshold should be 13 years of age."

Three details set the scope.

  • The subject of the first sentence is "countries." The paragraph sits under a heading addressed to government regulators, and it asks them to deliberate. It doesn't address parents or teachers.
  • The thing limited is "independent conversations." A child using a chatbot alone is covered. A teacher using an AI tool with a class of ten-year-olds, or a parent sitting beside a child, is outside the wording.
  • Thirteen is a "minimum threshold." The guidance sets a floor and leaves countries free to choose a higher age.

Step 2: Identify the reasons given

The paragraph opens with its reasons. Most generative AI applications "are primarily designed for adult users." They "often entail substantial risks for children, including exposure to inappropriate content as well as the potential for manipulation." The guidance adds a third reason, "the considerable uncertainty" that still surrounds these applications, and concludes that age restrictions "are strongly recommended for general-purpose AI technologies in order to protect children's rights and wellbeing."

The paragraph then explains where the number 13 comes from, and the explanation is candid. At the time of writing, the terms of use of a leading chatbot required users to be at least 13. That threshold derives from a United States law of 1998 on children's online privacy, passed long before chatbots existed. The guidance reports that "many commentators understand this threshold to be too young" and have argued for 16. It notes that European data protection law sets 16 as the age for using social media without a parent's permission.

So the guidance doesn't present 13 as an age established by research on children and AI. It takes a number already in use, says that many people think it too low, and offers it as the least a country should accept.

Step 3: Read the surrounding recommendations

The age limit is one of seven elements, and it takes its meaning from the others.

The element just before it is on data privacy. It asks governments to account for the fact that using these tools "almost always involves users sharing their data with the GenAI provider," and to pass and enforce laws protecting personal information. The age limit and the privacy element share an origin, since the 1998 law behind the number 13 was a privacy law.

Both sit inside a wider frame set out in section 3.1, "A human-centred approach to AI." There the guidance says AI "should be at the service of the development of human capabilities." Later, in section 4.2, under the heading "Protect human agency," it asks education systems to "protect learners' intrinsic motivation to grow and learn" and to prevent uses of AI that would "deprive learners of opportunities to develop cognitive abilities and social skills."

Read this way, the age limit is a protective measure within an approach that is mainly about keeping people in charge of their own learning. The document's own short summary describes the step more mildly than the headline did. It says the guidance proposes that governments regulate these tools, including "considering an age limit for their use."

Step 4: Ask what is left to national authorities and schools

The paragraph ends with two sentences that hand decisions to others. "Countries will also need to decide if self-reporting age remains an appropriate means of age verification." Self-reporting means a user types in a birth date or ticks a box. And countries "will need to mandate the accountabilities" of AI providers for checking age and of parents or guardians for monitoring children's independent conversations.

So the guidance leaves open the actual age, how age is checked, and who answers for a breach.

Schools get one line of their own. In subsection 3.3.3, on institutional users, the guidance suggests they "consider implementing minimum age restrictions for the independent use of GenAI in the institution." The verb is "consider."

Step 5: Compare the guidance with what surveys report

RAND, a US research organization, surveyed students, teachers, and school leaders in 2025. It found that 54 percent of students from kindergarten through high school used AI for school, with use higher in high school than in middle school. Over 80 percent of students said their teachers hadn't explicitly taught them how to use AI for schoolwork, and 45 percent of principals reported having any school or district policy or guidance (Doss et al. 2025).

Middle school in the United States typically covers ages 11 to 14, so some of the students reporting use are below UNESCO's floor. The survey doesn't say whether they used AI alone or with a teacher, and that is the distinction the guidance turns on.

The guidance pictures governments setting thresholds and providers verifying ages. The survey describes many students using AI with little instruction and, in more than half of schools, with no reported policy.

Key Considerations

The guidance uses "should" throughout. In UNESCO documents of this kind, "should" marks a recommendation to member countries. UNESCO can't make law for any country, and it has no authority over a school or a company.

The common mistake is to cite the age limit as if it were law, as in "UNESCO bans chatbots for under-13s" or "it's illegal for my eleven-year-old to use this." Whether any age limit binds a child depends on the law of the country they live in and on the terms each company sets for its own product. The guidance recommends that countries make such rules. It doesn't make them.

A second mistake is to drop the word "independent." The recommendation doesn't cover supervised use in school.

The document dates from September 2023. Company terms and national laws have changed since, so the particular product terms it describes shouldn't be assumed to be current.

Summary

The passage recommends an age floor to governments, gives protective reasons for it, admits the number is borrowed, and leaves the decisions that would make it real to countries and institutions.

UNESCO's 2023 guidance recommends that countries set a minimum age, no lower than 13, for children to hold conversations with generative AI tools on their own. The recommendation is addressed to government regulators, with a milder suggestion that schools consider limits of their own. Its reasons are that these tools were designed for adults, can expose children to inappropriate content and manipulation, and remain poorly understood. Each country decides the actual age, how it is verified, and who is accountable, because the guidance is advice and binds no one.

References

  • Doss, Christopher Joseph, Robert Bozick, Heather L. Schwartz, and 5 others. 2025. AI Use in Schools Is Quickly Increasing but Guidance Lags Behind: Findings from the RAND Survey Panels. RR-A4180-1. Santa Monica, CA: RAND, September 30, 2025.
  • Miao, Fengchun, and Wayne Holmes. 2023. Guidance for Generative AI in Education and Research. Paris: UNESCO, September 7, 2023.

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Guided Conversation 12 min

Set a Policy for One Assignment

In this conversation you'll pick one assignment or training task you know and work out how three different positions on AI in education would treat it. You'll leave with a rule for that one task and the evidence you'd give for it.

You'll have this conversation with an AI assistant, using your own account. Choose a button to open a new chat with the prompt already filled in, then press send to start. If the chat opens empty, copy the prompt and paste it in.

Run this conversation in whichever assistant you already use:

Claude desktop app

To use another LLM, simply copy and paste the prompt into its chat window.

Show the full prompt (it lists misreadings to watch for, so skip it if you would rather come to the conversation fresh)
Guided Conversation: Set a Policy for One Assignment (about 12 minutes)

Note to the learner: press send to start. Everything below is facilitator guidance for the AI. It lists misconceptions to watch for, so skip it if you'd rather come to the conversation fresh.

Please facilitate a reflective dialogue with me. I'm an adult with no technical background who has used AI chatbots for everyday tasks, and I'm studying the debate over AI in schools and universities. Follow this guidance for the whole conversation.

GOAL
I can compare positions on AI in education and the evidence on learning that each relies on, by applying them to one assignment I know.

HOW TO RUN THE CONVERSATION
- Ask one question at a time, then wait for my reply. Keep each of your turns under about 120 words.
- Don't lecture. Explain a point only when I need it to continue, then return to my assignment.
- Be curious and collegial. Use plain words and define any technical term briefly on first use. Welcome disagreement when I give a reason.
- Plain conversation only: don't search the web or create files or documents.
- Don't ask for any student's name, work, or grades, or anything confidential, and remind me not to share any if I start to.
- Aim for about 12 minutes. Spend most of the time on topics 2 and 3. If my replies are brief, offer one concrete prompt, such as "What should someone be able to do alone after finishing this task?" and move on. If I seem uncertain, shorten the conversation to 5-7 minutes. Always reach the final topic.
- Start now. Open with one or two warm sentences: this is a conversation, not a quiz; my reasons matter more than landing on a right policy; I can ask you to clarify anything. Then ask me to describe one assignment or training task I know, as a teacher, parent, student, or employee, and what it's meant to teach.

TOPICS, IN ORDER
1. The task and its purpose. Ask what the assignment is and what a person should be able to do afterward without help. Draw out the difference between finishing the task and learning from it.
2. Three positions. Take each in turn and ask me how I think it would treat AI use on this task before you add anything. (a) Tutor for every student: well-designed AI can teach each learner individually. (b) Threat to learning: in ordinary use, AI does the thinking the task was meant to exercise. (c) Assessment reform: if AI can complete the task, change the task. State each as its advocates would.
3. Which evidence applies. Ask which research findings bear on my task and which don't. Help me notice differences in subject, age group, and the kind of AI tool involved.
4. Closing. Ask me to state the rule I'd set for AI use on this one task and the evidence I'd rely on. Tell me I can take both into a short optional journal entry.

KEY POINTS TO KEEP ACCURATE
- Use is widespread. In a 2026 UK survey, 95 percent of undergraduates reported using AI in some way. In 2025 US surveys by RAND, about half of school students and teachers used it for school, and fewer than half of principals reported a policy.
- Guided tutoring and unguided answer-giving have shown opposite effects. In a field experiment in high school mathematics, students with an ordinary chatbot scored higher in practice and lower on a later exam without it; a tutoring version that gave hints avoided most of the harm. In a trial in one university physics course, a purpose-built AI tutor produced more learning in less time than an active-learning class.
- A 2026 OECD review concluded that completing tasks with AI doesn't necessarily translate into learning, and that design and task matter more than access.
- The evidence covers few subjects and short periods. Long-term and system-wide effects are unknown.
- Guidance from UNESCO and the US Department of Education (both 2023) recommends keeping people in control. These documents are advice and bind no school.
- Surveys report what people say they do. They don't measure learning.
- You map positions and evidence. You don't recommend a policy.

MISCONCEPTIONS TO CORRECT GENTLY
When one appears, name the accurate version briefly, then return to my assignment.
- "Banning it solves the problem": surveys show use continues and that rules lag behind it.
- "AI tutors are proven to work": the supporting trial tested one carefully designed tutor in one course.
- "Detection software can enforce a ban": tools that claim to detect AI-written text are unreliable and can flag honest work.
- "Students say it helps, so they're learning more": feeling helped and learning are different things.

LIMITS
- Don't recommend a policy or tell me which position is right. If I ask, say that you're mapping the positions and return the question to me.
- Don't favor or disparage any company or product, including the company that built you and its products. Don't recommend any AI tool.
- Give no legal advice. If I ask what my school's rules or the law require, tell me to check the rules that apply to me.
- Don't introduce jobs, energy use, or regulation.

TO FINISH
After my closing answer, close in one short turn:
- Affirm one specific thing I worked out, in my own words where possible.
- Suggest one or two next steps that fit how the conversation went. Possible steps: write a journal entry defending one position; ask a teacher or student how the task is handled now; redesign the task so it shows a person's own thinking.
- Restate my rule and my evidence on their own lines, labeled "My rule for this task" and "The evidence I rely on", so I can copy them.

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Journal 15 minOptional

The Position You'd Defend to a School Board

Overview

You'll choose one position on AI in education and write the case you'd make for it to a school board, along with the best objection and what would change your mind. Writing all three keeps the case honest.

The entry is optional. It's for you, and nobody collects it.

Writing Prompt

Choose one of three positions on AI in education: that AI can be a tutor for every student, that AI is mainly a threat to learning, or that the real task is to reform assessment. State it fairly, give the evidence for it, and say what would change your mind. Write 250–400 words.

Steps

  1. State the position in two sentences. Write it as someone who holds it would. If your wording would make an advocate object, revise it.
  2. Give the best evidence for it. Name a specific study, survey, or guidance document and say what it found or recommended. Say what kind of evidence it is, such as an experiment, a survey of what people report, or a recommendation. You can draw on any notes of your own.
  3. Give the strongest objection. Write the objection a well-informed opponent would raise, at full strength. An objection you can knock down in one line isn't the strongest one.
  4. Say what finding would move you to another position. Describe a result that could actually be observed, such as a study in a particular subject or age group coming out a particular way. Say which position you'd move to.

Self-Check

Before you finish, check that your entry:

  • States the position in a form its advocates would accept
  • Cites a specific study or document as evidence
  • States an objection at full strength
  • Names evidence that would change your view

Nothing is uploaded. Write in your own notebook or document and keep it.

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Knowledge Check 10 min

AI in schools and universities

This ungraded knowledge check assesses your understanding of the debate over AI in education and the evidence behind it. You'll be asked about survey evidence on use, institutional guidance, the difference between performance and learning, and the three main positions.

Note: Use this to test yourself, review the feedback on any questions you miss, and retry until you feel confident before moving forward.

5 questions · ungraded · retry as often as you like

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