AI for Reading Comprehension: 5 Ways to Build Deep Reading

Students reading together around a table, illustrating focused reading and discussion

Students can ask an AI chatbot to explain a difficult paragraph, define unfamiliar words or generate questions about a text. Those uses can make reading more accessible. But when a chatbot replaces the act of reading—by summarising the assigned chapter before a student has opened it—the learner may finish the task without developing the comprehension skills the task was meant to build.

New international evidence makes this distinction especially important. The OECD’s PISA 2025 Results, released on 8 September 2026, documents a continuing decline in reading performance and a rise in rushed, inaccurate reading. At the same time, a 2026 network meta-analysis of 56 studies finds that the reading medium matters: paper generally supports comprehension better than small screens that require scrolling. Together, these findings point to a practical question for schools: how can teachers use AI for reading comprehension without allowing it to do the reading for students?

What the PISA 2025 reading results actually show

PISA 2025 assessed more than 760,000 students across 91 countries and economies. Across OECD countries, average reading performance fell by 14 points between 2022 and 2025, and by 28 points between 2015 and 2025. The OECD also reports that the proportion of students classified as “hasty readers”—those who answer quickly but inaccurately—increased from 6.6% in 2018 to 11.4% in 2025. The share of accurate, fluent readers declined by seven percentage points over the same period.

The difficulty is not simply that students cannot locate a fact in a short passage. OECD analysis identifies particular weaknesses in tasks involving longer texts, evaluating information and connecting evidence across sources. These tasks require readers to hold ideas in mind, compare claims, infer meaning and decide whether a source is credible. They are precisely the processes that can be bypassed when a student accepts an AI-generated summary as a substitute for the original text.

These results do not prove that AI caused the decline in reading. PISA describes a complex picture involving reading habits, digital distraction, school conditions and wider social changes. The responsible conclusion is narrower: schools need to protect sustained reading and critical evaluation while teaching students how to use AI deliberately.

AI use is widespread—but frequency is not the same as learning

The same PISA cycle asked students how they used AI chatbots for schoolwork. On average, 46% of students in OECD countries reported using chatbots to help them learn at least weekly. The OECD found that students who used AI moderately for general learning purposes tended to perform slightly better than those who used it rarely or very intensively. Students who used AI very frequently or only occasionally tended to score lower than non-users in the relevant comparisons.

This is an observational relationship, not proof that a particular level of AI use produces better or worse results. Students differ in prior knowledge, motivation, school context and reasons for turning to a chatbot. The OECD itself cautions against interpreting these patterns as causal effects. For teachers, the useful message is that purpose and instructional guidance matter more than access alone.

For a broader look at the same PISA evidence on student chatbot use, see our analysis of PISA 2025 AI use in schools. This article focuses on a different question: how to design reading activities so that AI supports comprehension rather than replacing it.

Why the reading medium still matters

AI-supported reading often happens on a phone or laptop, but the screen itself can change the reading experience. A 2026 open-access network meta-analysis by Virginia Clinton-Lisell and Christine Litzinger compared paper with computers, tablets, e-readers and smartphones. It synthesised 56 independent studies and 79 effect sizes.

Across the comparisons, paper ranked highest for reading comprehension, followed by tablets, e-readers, computers and smartphones. The most important qualification was scrolling: when students had to scroll through a text, paper showed a meaningful comprehension advantage over digital devices. When scrolling was not required, the differences between paper and digital formats were small and not statistically reliable.

This does not mean that every student should read every text on paper. It suggests a practical design choice: use a stable, paginated layout for longer or more demanding texts, avoid forcing students to navigate long passages through continuous scrolling, and let students choose a suitable format when the learning objective allows it. The 2025 meta-analysis on leisure digital reading and comprehension also indicates that the relationship between digital reading habits and comprehension is nuanced, rather than a simple claim that all screen reading is harmful.

Five ways to use AI for reading comprehension without outsourcing the reading

1. Make the first encounter with the text independent

Before opening an AI tool, ask students to read the passage themselves. They can underline the central claim, mark two unfamiliar terms and write one question in the margin. This short independent first pass gives the learner something to compare with the chatbot’s explanation. It also gives the teacher evidence of what the student noticed before receiving assistance.

For a challenging text, the first pass does not need to produce a polished summary. A rough annotation is more useful than a fluent answer generated by a machine because it reveals the learner’s initial understanding.

2. Use AI to clarify a barrier, not replace the passage

Students can ask an AI reading tutor to explain one sentence, define a term in context or unpack a difficult reference. Keep the question narrow. For example:

“Explain the meaning of this paragraph in simpler language, but do not summarise the whole text. Identify the sentence that supports your explanation, and tell me which words in the original passage carry the main idea.”

The student should then return to the original passage and check whether the explanation is accurate. If the chatbot invents context or misreads the author’s position, the discrepancy becomes a useful lesson in verification.

3. Turn summaries into questions that require evidence

Instead of asking AI to write the summary students will submit, ask it to generate three comprehension questions at different levels: one about explicit information, one about inference and one about evaluating the author’s evidence. Students answer in their own words and cite the relevant paragraph or sentence.

This shifts the tool from answer production to question generation. It also helps teachers distinguish between locating information and the deeper work of interpreting and evaluating it—an important distinction in the OECD’s reading analysis.

4. Compare sources rather than accepting one fluent response

For older students, provide two short sources that disagree or emphasise different evidence. Ask the AI to list the claims made by each source, then require students to verify every claim against the originals. They should identify what the sources agree on, where they differ and what information is missing.

The final judgement must belong to the student. A chatbot can help organise a comparison, but it cannot replace the learner’s responsibility to assess authorship, evidence, date, purpose and reliability. This activity also builds the information-evaluation skills that matter beyond school assignments.

5. Close the chatbot and retrieve the meaning

After the AI-supported stage, students should put the tool away and reconstruct the argument from memory. They might write a three-sentence explanation, draw a concept map, answer a new question or explain the text orally to a partner. The teacher can then compare this independent response with the student’s earlier annotations.

Retrieval is important because recognising an AI explanation is not the same as being able to recall and use the underlying idea. Our guide to AI and student memory explores how retrieval practice can protect learning when digital tools are available.

A short classroom routine teachers can use tomorrow

For a 30-minute lesson built around a 500–700-word text, try this sequence:

Minutes 0–7: Students read independently, annotate the main claim and mark one point of confusion.
Minutes 7–12: Students use AI to clarify one selected passage, then verify the explanation against the source.
Minutes 12–20: Students answer an inference question and an evidence-evaluation question, citing the text.
Minutes 20–26: Students close AI and write a short explanation from memory or discuss it with a partner.
Minutes 26–30: The teacher checks a sample of responses and asks students to explain which evidence changed or strengthened their interpretation.

For a high-stakes or individual assessment, add a brief oral explanation or an in-class annotation check. Our article on oral assessment in the AI era offers practical formats for checking what learners can explain independently.

What the Morocco data add to the discussion

The OECD’s Morocco country note makes the issue concrete. In PISA 2025, 13% of students in Morocco reached at least Level 2 proficiency in reading, compared with an OECD average of 69%. The note also reports that 45% of Moroccan students used AI chatbots to help them learn at least weekly, while 30% used them to summarise a text they had to read. These figures describe reported behaviour and proficiency; they do not show that chatbot use caused the reading results.

The implication is not to reject AI. It is to ensure that students who use AI frequently still receive regular opportunities to read complete texts, explain ideas without assistance, compare evidence and ask teachers or classmates for help. AI should widen access to explanation without narrowing the amount of thinking students do themselves.

How schools can tell whether AI is helping

Schools should evaluate reading outcomes, not simply count chatbot logins or the number of AI-generated activities. A small set of indicators can make implementation more meaningful:

Independent comprehension: Can students answer a comparable question without AI after the activity?
Evidence use: Do they cite accurate details from the original text rather than repeat a chatbot’s unsupported claim?
Inference and evaluation: Can they explain why an interpretation is stronger and assess the credibility of a source?
Transfer: Can they apply the same strategy to a new text that the chatbot has not summarised?
Equity and access: Do students with different reading levels, languages and device access benefit from the routine?

Teachers should also explain to students that AI can make confident mistakes, omit qualifications or flatten an author’s argument. A useful classroom norm is simple: every AI explanation about a text must be checked against the text itself.

The principle: AI should deepen the encounter with a text

The PISA 2025 reading findings are a warning against confusing fast task completion with deep comprehension. The evidence on digital reading adds a reminder that format and navigation can influence how carefully students process longer material. Neither source justifies a blanket ban on AI or screens. Both support more deliberate instructional design.

Used well, AI can make a difficult paragraph more accessible, generate questions, support vocabulary and help students compare interpretations. Used poorly, it can remove the very cognitive work that reading lessons are designed to develop. The test of a successful AI-supported reading activity is therefore not whether the chatbot produced a good summary. It is whether the student can understand, question, explain and transfer the meaning after the chatbot is closed.