Failing grades soar with AI usage, dwindling math skills in Berkeley CS classes
dailycal.org
June 4, 2026
5 min read
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59/100
Summary
Failing grades in UC Berkeley's computer science classes reached 35.3% for CS 10 and 10.6% for CS 61A in spring 2026, significantly higher than previous semesters. Instructors attribute this increase to greater reliance on AI, insufficient mathematical preparedness, and understaffing.
Key Takeaways
- The percentage of failing grades in UC Berkeley computer science classes rose significantly in spring 2026, with 35.3% of CS 10 students and 10.6% of CS 61A students receiving F's, compared to less than 10% in previous years.
- Instructors attribute the increase in failing grades to students' reliance on AI, a lack of mathematical preparedness, and a rise in academic dishonesty.
- Nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026, highlighting concerns about students' readiness for assessments.
- Faculty members are advocating for the reinstatement of standardized testing for STEM admissions due to concerns about students' mathematical skills.
Community Sentiment
Positives
- Some users view LLMs as a powerful tool for learning, suggesting they can enhance understanding when used appropriately, rather than simply replacing critical thinking.
- The discussion highlights a potential need for a societal adjustment in how we integrate AI into education, suggesting that future generations may adapt better to these tools.
Concerns
- There are significant concerns about the cognitive decline in students and professionals who overly rely on LLMs, indicating a potential loss of essential skills like deep thinking and problem-solving.
- The suggestion to ban generative AI for younger users reflects a fear that reliance on these tools could hinder the development of foundational skills in mathematics and coding.
