In recent years, the arrival of generative artificial intelligence (AI) tools — from chatbots to writing assistants — has been heralded as a revolution for education. But a growing chorus of educators, researchers and students argue that even basic use of these tools may undermine the very skills schools are meant to build: reading, thinking, writing, reasoning. The New York Times opinion piece highlights this risk. Here we go deeper: what’s happening, why it matters, and where the hidden pitfalls lie.
What the original piece argued
The Times piece argued that when students rely on AI for tasks like summarising texts, drafting essays or generating responses, they may skip critical stages of learning: deep reading, critical thinking, mental effort and internalisation of ideas. It warns that ease may trump challenge, convenience may replace struggle, and students may become consumers of AI output rather than creators of their own ideas.
Expanding the picture: deeper layers of concern
1. Cognitive skill erosion
Research indicates that when students off-load mental effort to AI, certain cognitive skills atrophy. For example, students who use AI to generate essays may engage less in planning, structuring, revising and reflecting on their own writing. A recent higher-education study found that ChatGPT use in a physics course correlated with lower performance when students then had to solve problems unaided.
2. The “shortcut culture” effect
AI tools offer quick answers, polished text, instant feedback. That convenience can encourage a mindset of “get it done” rather than “learn how to do it”. Over time, students may rely on AI for surface-level outputs rather than wrestle with underlying concepts—especially in reading, interpreting and writing.
3. Academic integrity and the illusion of learning
Using AI to write or summarise may produce acceptable grades, but it doesn’t guarantee real comprehension. Students may perform without understanding, creating an illusion of learning. Many educators warn that this trend may erode long-term retention, deep understanding and the ability to think independently.
4. Equity and access issues
AI tools may amplify existing educational inequalities. Students with access to premium AI tools, prompt-engineering skills or private tutoring will gain disproportionate advantage, while students without may be further left behind. Moreover, when teachers rely on AI to “help” students, weaker students may become more dependent on automation instead of building foundational skills.
5. Curriculum misalignment and teacher readiness
While AI tools proliferate, many schools lack policies, teacher training or curriculum design that integrates AI appropriately. One national survey found that although over half of students and teachers reported using AI, far fewer schools provided guidance on how to use it for learning instead of as a shortcut.
6. Long-term implications for work and lifelong learning
Education is meant to develop learners who can think, adapt, reason and create. If students increasingly rely on AI for thinking-for-them, they may enter higher education or the workforce with weaker foundations. Some researchers argue that generative AI may redefine what “thinking” means, but only if students continue doing the thinking—not the machine.
Why This Matters Now
- Rapid adoption: More students are using generative AI tools for homework, papers, projects. Educators report a steep rise in use over the last 1-2 years.
- Mismatch with assessment and pedagogy: Traditional tasks (essays, take-home tests) may no longer demand tasks that AI cannot do. Without redesign, students can rely on AI to complete assignments without genuine learning.
- Regulatory and institutional lag: Schools and policymakers are scrambling to define what counts as cheating, what is allowed, and what is beneficial. In many places, guidance is missing or inconsistent.
- Societal stakes: The shift isn’t just educational—it touches on citizenship, critical literacy, ability to discern and reason. In a world where information flows fast, the ability to think deeply matters more than ever.
What the Original Coverage Didn’t Fully Explore
- Neuroscientific and long-term cognitive impacts: Some studies show that students relying on AI show reduced neural engagement in writing tasks, lower memory retention and less effortful reflection. More longitudinal research is needed.
- Teacher practice and pedagogy redesign: How are teachers changing tasks, prompts, classroom design to ensure students still do deep thinking? Some case studies show creative redesign (e.g., AI-vs-human essay comparison) but the scale is limited.
- Differential impact across subjects: AI may affect reading-writing tasks differently from STEM, arts, languages. For example, AI still struggles with deep proofs or original research, so subject-specific risk is uneven.
- Student mental-health and motivation: When students feel they don’t need to work hard (because AI can do it), motivation, persistence and self-efficacy may decline. Some students report a feeling of “just-getting-through” rather than learning.
- Ethical and privacy issues: When students submit prompts or work via AI, who owns their data? Are models biased? Does AI-generated assistance create a surveillance culture?
- Global and resource-limited contexts: Many reports centre on well-resourced schools in developed nations. How these dynamics play out in low-resource settings is underexplored—where basic literacy, access and teacher training already challenge educators.
Recommendations for Educators, Students and Policymakers
- Redesign assignments: Focus on tasks that require personal reflection, unique reasoning, process-documentation, oral defence or handwritten work—things less easily outsourced to AI.
- Teach AI-literacy: Educate students not just about how to use AI, but when not to use it. Embed prompts like “Why did I ask this question?” or “What did I learn?”
- Use AI to augment, not replace: Encourage students to use AI as a tool (idea generation, revision, research help) but ensure they still do the thinking, writing, reading and revision themselves.
- Clear policies and norms: Schools should provide transparent guidelines: what is acceptable AI assistance, what constitutes misuse, how to cite it, how to show individual thought.
- Professional development: Teachers need training to recognise AI-helped work, adjust feedback, redesign tasks and support student reflection rather than simply grade it.
- Equity-focused access: Ensure access and guidance so that all students—not just those tech-savvy or resourced—can use AI tools effectively and ethically.
- Monitor long-term outcomes: Researchers and institutions should track how AI use is affecting student learning, retention, cognitive skills and preparedness for higher education/work.
Frequently Asked Questions (FAQ)
Q: Isn’t AI just a tool — why is it “bad” for students?
A: AI can be a tool—but when it does the thinking, reading or writing for the student, the student misses the essential learning process. Skills like planning, reasoning, drafting, evaluating and revising are essential for deeper learning; if AI bypasses them, the student doesn’t build those skills.
Q: Are there any benefits of AI for students?
Yes. AI can help with brainstorming ideas, offering feedback, providing alternate explanations, helping with revision, summarising texts—but only when the student uses it deliberately and still engages actively. The key is augmentation, not replacement.
Q: What kinds of assignments are most at risk?
Tasks like take-home essays, summarising a reading, generating a response prompt, writing a standard report—especially when the same prompt is reused and when evaluation focuses purely on final output rather than process. Tasks that require doing, thinking, arguing, revising are less at risk.
Q: Can schools just ban AI tools?
Bans are one response, but they have limitations. They may drive students underground or simply shift the shortcut. A more effective approach is integrating AI-literacy, redesigning tasks and focusing on student thinking. However, clear policies about misuse are required.
Q: How should students approach AI?
Students should ask: “Does this tool help me learn, or is it doing the work for me?” Use AI for support, not substitution. After using AI, students should review, reflect, personalise and revise—ensuring the thinking remains theirs.
Q: What if I’m preparing for college or work where AI will be standard?
Good point. Indeed, AI will become part of many workplaces. But that means students must still learn the underlying skills: reading complex material, thinking critically, writing clearly, collaborating, iterating. Once these are mastered, AI becomes a tool—rather than a crutch.
Q: Will reliance on AI make students less employable?
Potentially yes—if students lack foundational skills and the ability to perform without AI. Employers value problem-solving, original thinking and adaptability. If students are accustomed to turning automation on by default, they may struggle when required to lead, create or adapt.
In Summary
AI is not inherently bad for students—but unreflective, routine use of AI by students poses serious risks. The real danger lies not in the tools themselves, but in the short-circuited learning process: when students bypass reading, reasoning, drafting, revising and reflecting. The challenge for education is to ensure AI becomes a partner in learning—not a shortcut that erodes learning.
For students, educators and policymakers alike, the message is clear: think about how we use AI, not just whether we use it. In skipping the hard parts of thinking, we may lose what schools exist to teach.
Sources The New York Times
