Generative AI arrived in education everywhere all at once. Today’s students are surrounded by tools that promise help, answers, and efficiency at every turn. But learning has never been about convenience alone. As AI reshapes how students engage with academic material, the questions are whether it is being built to support how humans actually learn and ultimately improve outcomes.
New research on millions of actual higher education student interactions in digital course materials suggests that the answer lies in a deceptively simple idea: active reading (and in AI designed to support it, not replace it).
Active reading is a well‑established concept in learning science. It describes how effective readers interact with text: testing their understanding, highlighting key ideas, asking questions, taking notes, and revisiting challenging concepts. These behaviors are strongly associated with better comprehension, retention, and academic performance. Reading, after all, is not a passive act. It is cognitive work.
AI TOOLS CAN ENGAGE STUDENTS
Yet digital learning environments, and now many AI tools, too often encourage the opposite: Skimming. Outsourced thinking. Letting the machine do the synthesis and interpretation work for the learner.
An analysis of nearly 80 million student interactions across Pearson eTextbooks aligned to college courses over two semesters helps us understand how students actually behave when AI tools are built responsibly into learning materials. The findings were striking. Students who used these AI study tools were dramatically more likely to engage in active reading behaviors than those who did not.
When students used AI study tools in their eTextbook, they were three times more likely than non-users to be active readers. Further, the data showed that students who used AI tools built into instructor-led digital platforms with assessment features and other interactive tools were over 20 times more likely to be classified as active readers, compared to non-users.
This matters because reading remains one of the strongest predictors of college success, and because readiness is declining. National data from the National Assessment of Educational Progress shows that fewer than two-thirds of incoming students are prepared for college‑level reading, while faculty report growing struggles with close reading and analysis. The challenge is not access to content, but engagement with it.
What distinguishes effective learning AI from the wave of general-purpose apps is intent. AI-enabled reading features designed with learning science in mind do more than generate immediate answers to homework questions. They offer struggling readers short, accessible summaries of the text to aid in comprehension; they provide clarification on confusing concepts; and they offer students the opportunity to practice retrieving information from memory, which we know is beneficial for long-term retention. In other words, it augments human learning.
RESPONSIBLE AI LOOKS DIFFERENT
This is where many consumer AI tools fall short. They are impressive at generating text and images, but indifferent to whether learning occurs. Without trusted content or pedagogical design, speed becomes the goal; depth becomes collateral damage.
Responsible AI in education looks different. It is transparent. It is trained and evaluated against expert-vetted content. And it is embedded within high-quality, trusted content that instructors and institutions already rely on. When AI is designed this way, it pulls students back into reading.
The future of education will undoubtedly be shaped by AI. But the most important innovation is using technology to help students do the hard, essential work of learning more effectively.
If AI can turn passive consumption into active engagement, it will change what students are capable of understanding and it will change outcomes. That’s the result we should all be pursuing.
Tom ap Simon is the president of Pearson Higher Education and Virtual Learning
