How Artificial Intelligence is Dumbing Down Education

A growing concern about artificial intelligence in higher education isn’t simply that students will sail through exams by cheating — it’s that AI may eventually let students get through university without engaging deeply in learning. Public debates often focus on worries that students might use AI to cheat on assignments or write essays for them, but this misses a far more consequential shift already underway.

AI tools are being adopted widely across campus life, from admissions and scheduling to academic advising and risk assessment. These systems don’t just improve productivity — they can count towards outcomes like optimized course schedules or identifying at-risk students. Students rely on AI to summarize texts and help them study, faculty use it to build syllabuses and create assignments, and researchers rely on it to write code and review literature.

As machines become better at generating research, classroom materials, and even feedback, universities risk being left behind in their traditional roles of teaching and mentorship. What then counts as academic rigor if AI can do much of the work that humans used to struggle through?

Some people worry that heavy reliance on AI tools will count against students’ development because they may miss opportunities to grapple with difficult texts, refine arguments, or solve problems independently. Instead of poring over primary sources or original research, learners may simply pore over AI summaries and accept them at face value.

This shift can also affect how knowledge itself is valued. Once AI systems can produce essays, conclave unstructured arguments, and compress hours of work into minutes, the kind of in-depth engagement that historically defined undergraduate and graduate learning might come across as unnecessary.

If students fall behind in building real expertise because they rely on shorthand AI outputs, universities will have succeeded only in producing credentials — not genuine understanding. Some educators fear that graduates with impressive degrees but little ability to think critically will end up barely competent in workplaces where deep analytical skills are expected.

There’s also the question of authorship and accountability. When AI drafts parts of a paper, who is responsible for its content? If the work is machine-generated, should it still count towards a degree requirement, or does it merely let students scrape through assessments?

Taken together, these developments suggest that the greatest risk of AI in higher education isn’t just cheating — it’s that students may scrape through academic programs without mastering the very skills universities once held essential. That's not a transformation universities can afford to ignore as they decide how to adopt these technologies.

Vocabulary Guide

Below are your phrasal verbs with definitions, example sentences, and practice discussion questions to deepen understanding.

to sail through

Definition: To succeed easily or with little effort.Examples:

• Some worry students might sail through essays with AI help.

• She sailed through her exams thanks to weeks of preparation.

• If you know the material well, you can often sail through tests.

Discussion:

• What subjects do you think someone could sail through with little effort?

• Have you ever sailed through a test or project? Why?

to get through

Definition: To survive or complete something difficult.Examples:

• Many learners can get through readings faster with AI summaries.

• I didn’t enjoy the course, but I managed to get through it.

• She got through the long lecture by taking careful notes.

Discussion:

• What strategies help you get through a tough assignment?

• Do you think getting through challenges is more important than sailing through?

to major in

Definition: To specialize in a particular academic subject.Examples:

• Many students major in STEM fields.

• She originally planned to major in history but switched to philosophy.

• I’m majoring in economics next semester.

Discussion:

• What subject would you like to major in and why?

• Does AI change what subjects people choose to major in?

to count towards

Definition: To be included as part of something required (like a degree).Examples:

• AI lab participation counts towards the final grade.

• Attendance counts towards class participation.

• This assignment won’t count towards your GPA.

Discussion:

• Should AI-assisted work count towards a degree? Why or why not?

• How do you decide what should count towards your final grade?

to be left behind

Definition: To be overtaken or fall out of progress compared to others.Examples:

• Universities risk being left behind if they ignore AI impacts.

• Students who skip practice can be left behind by their peers.

• If you don’t study, you’ll quickly be left behind.

Discussion:

• When have you felt left behind in a class or skill?

• How can institutions avoid being left behind by technology?

to count against

Definition: To be a negative factor in evaluating something.Examples:

• Excessive AI use might count against students’ learning skills.

• Missing homework often counts against a student’s score.

• Bad behavior can count against your application.

Discussion:

• What behaviors should count against a student’s grade?

• Can reliance on AI count against real learning?

to pore over

Definition: To study or look at something carefully.Examples:

• Students used to pore over texts that AI now summarizes.

• She pored over her notes before the exam.

• Researchers pore over data for hours.

Discussion:

• Do you prefer to pore over texts or use summaries? Why?

• How might AI change what students pore over?

come across

Definition: To find or encounter by chance.Examples:

• Students may come across interesting ideas while researching.

• I recently came across an article that changed my view.

• He came across an old photo in the library.

Discussion:

• What’s something valuable you came across unexpectedly?

• How does serendipity affect learning?

fall behind

Definition: To fail to keep up with required progress.Examples:

• Students might fall behind if they rely too much on AI.

• She fell behind after missing classes.

• Don’t let yourself fall behind in reading.

Discussion:

• What do you do when you risk falling behind in a subject?

• Can technology help prevent students from falling behind?

scrape through

Definition: To succeed with difficulty, barely passing.Examples:

• Some might scrape through degrees without real understanding.

• He scraped through his final exams by memorizing answers.

• She scraped through the course with lots of effort.

Discussion:

• Have you ever scraped through a test or course? What happened?

• Is it better to scrape through or fail and try again?