College instructors often encounter a familiar classroom issue: students can discuss a reading’s main idea but struggle to cite specific passages or understand the argument’s development. This surface-level familiarity often stems from AI-generated summaries. Tools like ChatGPT and NotebookLM provide quick overviews of complex texts. While helpful for review, they allow students to sidestep the cognitive process reading supports. Instead of restricting AI use, a simpler solution was tried: redesigning the reading environment to make engagement with texts more evident and accountable.
Reading is an active practice essential for discussion and analysis, yet instructors report increased difficulty capturing students’ attention for assigned texts. In AI-saturated classrooms, students access summaries and explanations without engaging with the original text’s language or structure. The issue isn’t just AI use but that reading itself is becoming invisible. Students participating using secondhand text representations lack incentive to engage deeply. The goal was to make reading visible again, ensuring students actively read rather than delegate the task.
The intervention was tested in a first-year writing program at a U.S. branch campus in the Middle East, where students complete courses focused on critical reading and argumentation. With small class sizes, reading practices are easier to monitor. Courses cover themes like identity and inequality, using various genres. It became evident students relied on AI-generated summaries, often knowing a text’s topic but struggling with its reasoning or language. Therefore, the focus shifted to redesigning the reading conditions instead of focusing on tool use.
At the semester’s start, students received a printed booklet containing all readings. Instead of PDFs or links, they used a physical packet. Students annotated readings by hand, highlighting ideas, underlining claims, and writing in margins. This material shift framed reading as a tangible activity rather than a screen-based task. Research suggests handwriting promotes selective attention and deeper processing, a pattern observed in practice. The booklet became a cumulative record of engagement, frequently referenced in class discussions and assignments.
To ensure active reading, the booklet was paired with an accountability mechanism. Before each class, students uploaded photos of their annotated pages to the university’s learning management system. These submissions weren’t graded but served as preparation evidence. Small class sizes made reviewing submissions manageable, and students quickly understood reading was expected and visible. This had a noticeable impact; students came prepared, referenced specific passages, asked text-based questions, and supported claims with evidence.
Throughout the semester, students reported reading more carefully and with greater focus. Many said working with printed text encouraged attention to language and structure, unlike screen reading. Class discussions became more text-grounded, with students citing page numbers and building on each other’s observations. In assignments requiring engagement with readings, students showed clearer textual evidence use and source integration. Reading became a visible component of intellectual work, not just a preparatory task.
Compiling course readings into a printed booklet involved coordinating with library staff to ensure all materials were covered under existing licenses or reserves. The booklet didn’t introduce new copyright issues; it consolidated approved readings. Instructors considering a similar approach should consult institutional guidelines, but assembling readings into a single format is often manageable and compliant.
This intervention doesn’t ban AI tools or label them problematic. Instead, it creates conditions where reading can’t be fully outsourced. By making text interaction visible and tangible, the booklet restores reading as a site for interpretation and meaning-making, which AI summaries can’t replace. The aim isn’t to return to print for its own sake but to use intentional pedagogical design. Changing learning’s material conditions alters student focus and what they can easily avoid.
Though developed for a first-year writing course, the intervention is adaptable to any course where reading matters. It requires no new technology, minimal ongoing labor, and little explanation to students. As generative AI reshapes student engagement with academic work, instructors don’t always need complex tech solutions. Sometimes, a low-tech redesign suffices to return the responsibility for meaning-making to students. María Pía Gómez-Laich, PhD, is an Associate Teaching Professor of English at Carnegie Mellon University in Qatar, where she researches academic writing and genre-based pedagogy.
Original Source: facultyfocus.com
