UCLA professor warns AI is creating an educational crisis for students

A UCLA sociology professor warns that artificial intelligence is fundamentally undermining human education and intellectual development, creating what he calls a “coming AI cataclysm” for society. The analysis suggests that while AI may boost productivity for those with existing skills, it threatens to create a generation unable to think critically or perform basic intellectual tasks without technological assistance.

The core problem: Students are increasingly using AI to complete homework and assignments, preventing them from developing essential cognitive abilities.

• OpenRouter data shows AI token usage drops dramatically during summer break and spikes when school resumes, indicating widespread academic cheating.
• OpenAI’s own analysis reveals 40% of all queries involve “doing” tasks like writing, editing, and summarizing—work traditionally done by humans.
• University deans report a “new normal of nonstop misconduct cases” related to AI usage in academic work.

Why detection is nearly impossible: Traditional plagiarism tools are ineffective against AI-generated content, creating an enforcement nightmare for educators.

• TurnItIn’s AI detector has significant false positive rates—if misconduct occurs 5% of the time and the detector has a 5% false positive rate, half of all flagged cases would be innocent students.
• Many universities decline to use AI detection tools due to these reliability issues.
• In-class assignments could solve the problem, but online education’s growth makes comprehensive proctoring impractical.

The skills gap dilemma: Effective AI use requires knowledge and abilities that can only be developed through traditional learning methods.

• Students caught cheating often produce obviously flawed work because they “are too ignorant and lazy to know what good output would look like.”
• Gabriel Rossman, the UCLA professor, notes his own successful AI use stems from “thirty years in higher education” and decades of learning without AI assistance.
• “Knowing what good output looks like requires skills and knowledge that can only be acquired the old-fashioned way, by doing one’s own work.”

What this means for the future: The analysis predicts a stark divide between those who can complement AI and those who become dependent on it.

• Workers educated before widespread AI adoption will become “the human capital equivalent to pre-war steel”—rare and valuable for their uncontaminated skills.
• Future generations may lack the foundational knowledge needed to effectively direct or evaluate AI output.
• Rossman warns: “We may run out of people with motivation and background to learn, know, and do.”

The broader implications: This educational crisis could destabilize social and economic structures as young people find their labor increasingly superfluous.

• AI “heightens the contradictions”—benefiting those with existing knowledge while spelling “total destruction for the system of universal education and credentialing.”
• The technology creates a choice for young people “to idle in stupidity and ignorance” rather than develop their capabilities.
• The professor concludes by questioning what happens “when the relationship between social classes changes rapidly and the young find their labor superfluous to the needs of capital.”