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The short answer is that current evidence does not show top students are avoiding reading because of AI. The claim in the headline is a plausible worry, and it is worth testing, but the main sources available in 2026 measure related things: how often students use AI chatbots, what tasks they use them for, how students perceive AI’s help with reading, and how reading scores have changed over time. None of them measures whether high achievers read less, and none establishes that AI causes any change in reading behavior.
This article separates what the data show from what the headline implies, and ends with a practical way to use AI while reading that keeps the text doing the work.
What the headline claims, and what would have to be shown
“Top students are avoiding reading” makes three separate claims. The first is that high-achieving students read less than other students. The second is that students are reading less overall. The third is that AI is the reason. Each needs its own evidence, and the sources available so far address none of them directly.
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What the OECD’s PISA 2025 data show
The OECD’s PISA 2025 Results (Volume I), published in 2026, is the largest source on student AI use in this comparison. It reports on students across OECD countries and links chatbot use to science performance, not to reading habits. Several findings are relevant to the question.
Use depends on the task
Students were asked how often they used AI chatbots for specific schoolwork tasks, including summarising assigned reading, conducting preliminary research, drafting writing, and helping them learn. The associations differed by task:
- Students who did not use chatbots for specific schoolwork tasks, including summarising assigned texts, tended to score higher in science than students who did.
- Students who used chatbots weekly for general learning purposes performed similarly, on average, to students who did not use them.
These are cross-sectional associations. They do not show that AI use lowered or raised science scores, and they say nothing about reading scores or reading habits.
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Frequency of general learning use
In OECD countries, 46% of students said they use AI chatbots weekly or more to help them learn. This is a frequency figure for general learning support. It does not measure how much of that use replaces reading, and it does not show that students who use chatbots are reading less.
Instruction in judging AI output
OECD reports that students who use AI frequently for learning and who have had school opportunities to assess AI-generated information tend to score slightly higher in science than non-users or infrequent users. Those learning opportunities are more common among socio-economically advantaged students. The finding points to access to instruction as an important context. It does not show that AI improves performance for every student.
The reading trend
The OECD average reading score fell by 28 points between 2015 and 2025. This is a population-level trend. The OECD foreword says the causes are uncertain and points to possible factors such as declining reading for enjoyment and a shift toward digital reading. It does not identify AI as a cause. Attributing the decline to chatbots would go beyond the evidence.
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Separately, 28% of students said that classmates were distracted by digital devices during most or every science class. That figure concerns devices in general, not AI.
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The National Literacy Trust’s 2024 survey covers young people in the UK and examines attitudes and behavior around generative AI and literacy. Its figures are self-reported, and they are most useful for understanding perceptions rather than outcomes.
- Survey scope: 53,169 young people in the overall survey. The report’s focus includes 15,830 young people aged 13 to 18 and 1,228 teachers across UK schools.
- Perceived help with reading: 23.2% of young people said generative AI helped them with reading. This is a reported perception, not a measured gain in comprehension.
- Demand for critical AI skills: 82.0% agreed that students should be taught how to engage critically with generative AI.
The gap between these two figures is the most useful point in the survey. Many young people feel AI helps them with reading, and most want to be taught how to question AI output. Neither finding tells us whether reading comprehension improved or fell.
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Comparing the kinds of AI use the evidence separates
The sources become more useful when AI use is split along four axes: the task, how often it happens, whether the student was taught to evaluate AI output, and what outcome was measured.
| Pattern of use | Frequency reported | Instruction in evaluating AI output | Outcome measured |
|---|---|---|---|
| Summarising an assigned text (PISA 2025) | Asked as a specific schoolwork task; frequency by task reported by OECD | Not stated as a separate factor in this comparison | Science performance, cross-sectional association only |
| General learning support, weekly or more (PISA 2025) | 46% of students across OECD countries | Linked to school opportunities to assess AI output, which are unevenly distributed | Science performance; similar on average to non-users |
| Preliminary research and drafting (PISA 2025) | Asked as specific tasks; frequency by task reported by OECD | Not stated as a separate factor in this comparison | Science performance, cross-sectional association only |
| AI helping with reading (UK survey, 2024) | Not stated as a frequency; 23.2% said AI helped | 82.0% said students should be taught critical engagement | Self-reported perception, not tested comprehension |
The table shows what each source can answer. No row measures how much a student reads, and no row compares reading volume across achievement levels.
Testing the premise: what a sound answer would require
A study that could support the headline’s claim would need to do several things the current sources do not:
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- Measure how much students read, for pleasure and for school, not only how they perform on tests.
- Compare reading volume across achievement levels, so that the behavior of high achievers can be seen directly.
- Record which AI tasks each student uses, and how often, alongside those reading measures.
- Use a design that can separate AI use from other factors such as home reading culture, school resources, and device use.
Until a study like this exists, the accurate answer is that the claim is untested. It may be true for some students and false for others, and the available data cannot say which.
How to use AI while reading without skipping the text
The evidence supports one practical distinction: AI can help a learner question and evaluate a text, or it can stand in for the text. The following routine keeps the reading in the student’s hands.
- Read the passage first. Read the assigned text in full, without AI, before opening a chatbot. Note one question and one tentative answer.
- Ask for questions, not summaries. Prompt the tool to pose three questions about the passage, then answer them yourself. Summaries are where the reading tends to get skipped.
- Check every claim against the text. For each statement the tool makes about the passage, find the sentence or paragraph that supports it. If you cannot find one, treat the claim as unverified.
- Compare your reading with the output. Write two sentences saying where the tool’s account differs from your own understanding, and why.
- Keep a reading log. Record the pages or chapters read without AI each week. A falling count is an early sign that AI is replacing reading rather than supporting it.
This routine does not guarantee better outcomes. It keeps the reading visible and gives you a way to notice when it is slipping.
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The evidence supports a narrow claim: AI use is associated with different outcomes depending on the task, the frequency, and whether students have been taught to assess AI output. It also shows that young people report AI helping with reading and that many want instruction in using it critically. The evidence does not show that top students are avoiding reading, that AI causes reading avoidance, or that AI explains the fall in OECD reading scores between 2015 and 2025.
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