Former students say assigned materials feel too long and “boring,” pushing them to use AI summaries instead. For colleges, the lesson is clear: course design and reading incentives must evolve.
A recurring message is landing in college classrooms—and it’s coming from students themselves: assigned materials often don’t get opened.
According to Misryoum, former students who now span different colleges recently told a simple story.. When they were assigned instructor-made texts or course materials. most classmates “didn’t read” them because they felt too long and too boring.. Instead, they say they copy the materials into AI tools to generate summaries—leaving the original text largely untouched.
That account points to a bigger shift in learning behavior, one that many instructors may sense but not fully measure.. Reading heavy course packs and textbook chapters were built for an earlier model of study. where the default expectation was that students would wrestle through the source first.. Today, AI summary features compress the first step.. The “effort” moves from reading the full material to evaluating whether the summary matches what the course expects.
Misryoum sees a direct implication for curriculum design: if the assigned content is not strongly motivating. students will look for the fastest path to comprehension—especially when academic norms now make AI tools easy to access.. Textbook authors often face market incentives that don’t always reward engagement over coverage. meaning the reading can feel like a hurdle rather than a learning experience.. In Misryoum’s view. the result is not simply a complaint about student laziness. but a mismatch between course delivery and how students actually process information now.
There’s also a contrast worth keeping in mind.. Misryoum notes that some learning materials have long been shaped to support readers who need extra scaffolding—particularly in English language learning contexts—where accessibility is treated as a quality requirement rather than an afterthought.. That approach can be instructive for mainstream courses too: if content is hard to engage with. the fix may not be more assignments. but better structure. clearer entry points. and shorter “chunks” that build momentum.
The classroom effect is likely to be uneven.. Misryoum’s analysis suggests that students who developed strong writing habits during earlier schooling years may use AI differently than those who came into higher education at the exact moment AI became mainstream.. If writing expectations and feedback loops were handled well in K–12 settings. students may be more willing to draft on their own.. But if their schooling accustomed them to AI as a default assistant. the jump to college may make reading and independent writing even more optional.
For instructors. this is where the conversation can’t stay stuck on the idea of “AI resistance.” Misryoum emphasizes that the more practical challenge is designing assignments that require engagement with the material in ways AI summaries can’t easily replace.. That can mean building tasks around specific interpretations. structured reflection. or iterative writing that depends on students demonstrating that they actually worked through the ideas.. Guardrails—when taught well—can turn AI from a shortcut into a learning tool. without erasing the core skills colleges are trying to assess.
There’s a human side to the change too.. Misryoum recognizes that time is not evenly distributed.. For students, every hour spent decoding long texts competes with everything else: commuting, part-time work, family responsibilities, and social life.. If AI summaries reduce the friction of getting “the gist. ” some students will naturally choose the time trade that feels most rewarding.. One former student described a likely motivation plainly: if they could get AI to do much of their writing. they would—at least sometimes—because it would free up time with friends.
That raises a wider policy question for higher education.. If students widely bypass core materials, colleges may need to revisit how they measure learning.. Reading compliance is no longer a reliable proxy for understanding.. Misryoum’s editorial take is that course teams may need to shift attention from whether students opened the assigned text to whether they can use the ideas accurately. apply them in new contexts. and justify their reasoning with evidence.
The bottom line for Misryoum is straightforward: this is a wake-up call. not only for publishers of long. dense materials. but for instructors responsible for course design.. The “new day” isn’t just about technology—it’s about relevance, clarity, and student motivation.. When learning design respects how students study today. AI can become part of the toolkit instead of the replacement for meaningful reading.
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