随着人工智能的使用,伯克利计算机科学课程的挂科率飙升,数学技能不断下降。
Failing grades soar with AI usage, dwindling math skills in Berkeley CS classes

原始链接: https://www.dailycal.org/news/campus/academics/failing-grades-soar-as-professors-see-greater-ai-usage-dwindling-math-skills-in-uc-berkeley/article_16fad0bf-02cb-4b8c-8d88-888ffd9f8608.html

2026年春季,加州大学伯克利分校电子工程与计算机科学系(EECS)的挂科率出现大幅飙升,远超部门官方指导标准。在CS 10和CS 61A等课程中,挂科率分别达到了35.3%和10.6%,而往年这一比例均低于10%。 讲师Dan Garcia和Gireeja Ranade指出了导致这一下滑的几个主要原因。其中最主要的是对人工智能工具的过度依赖,这不仅导致了普遍的学术不端行为,还使学生在应对考试时准备不足。此外,教师们反映学生愈发缺乏高级课程所需的数学基础能力,促使超过1300名教职员工呼吁在招生中恢复标准化考试。 加剧这些问题的还有系统性挑战,包括人手不足以及学生在答疑时间(office hours)参与度显著下降。教授们担心学生正在逃避深度学习所必需的“挫折感”。展望未来,教职员工正在重新审视教学方法,强调必须加强批判性思维和分析能力,以确保学生在人工智能时代仍具竞争力,并有能力应对复杂的现实挑战。

加州大学伯克利分校近期的一份报告指出,计算机科学课程的不及格率急剧上升。教职人员将其归因于过度使用人工智能以及基础数学能力的下降。 Hacker News 上的专业人士讨论提出了几个关键点: * **认知萎缩:** 许多专家认为,过度依赖大语言模型进行头脑风暴、编程和写作,导致了“深度思考”能力的丧失,以及独立解决难题能力的退化。他们将人工智能导致的智力衰退比作计算器普及后基础算术能力的丧失。 * **教育改革:** 教授们难以适应那些通过人工智能生成作业、从而绕过“学习挣扎”(掌握知识的必要过程)的学生。许多人建议转向口试、课堂监考,以及将人工智能用作苏格拉底式导师而非捷径的“翻转课堂”。 * **结构性问题:** 另一些人指出,不及格率的上升可能也反映了更广泛的系统性变化,例如取消标准化入学考试,以及计算机科学作为一种默认的高薪职业路径受到追捧,而非出于真正的兴趣或天赋。 总而言之,各方达成共识:虽然人工智能是高效的生产力工具,但如果学生没有先掌握基础知识,它就可能变成阻碍认知发展的“拐杖”。
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原文

The percentage of failing grades in multiple UC Berkeley computer science classes in spring 2026 is significantly higher than past semesters and marks a departure from the department’s grading guidelines.

Instructors point to students’ increased reliance on AI, lack of mathematical preparedness and understaffing as potential contributing factors.

According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026. In spring 2025 and spring 2024, the percentage of F’s did not exceed 10% for either class. The electrical engineering and computer sciences department’s grading guidelines state that 7% of students in lower division courses, including CS 10 and CS 61A, should receive D’s and F’s.

In addition, the guidelines state that “a typical GPA for a lower division course will fall in the range 2.8 – 3.3.” In spring 2026, both classes’ average grades were C-pluses, according to Berkeleytime, corresponding to a 2.3 GPA.

UC Berkeley teaching professor Dan Garcia taught both CS 10, “The Beauty and Joy of Computing,” and CS 61A, “The Structure and Interpretation of Computer Programs,” in spring 2026. Garcia believes the “primary driver” of these abnormally high failing rates is due to a “vast increase in academic dishonesty” due to students’ usage of large language models, such as Claude, ChatGPT and Google Gemini.

“Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the center for student conduct,” Garcia said. “But in other cases, it’s students who are leaning a little too hard on LLMs to do their work for them, and then at exam time just really aren’t ready.”

According to Garcia, nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026.

Neither of Garcia’s classes this semester was graded on curves; instead, each letter grade has a point threshold. This means that students’ grades do not depend on their peers’ performances.

Garcia believes that instructors “should not be curving” but should instead make thresholds for each letter grade publicly available and give students many chances to reach them. He added that he loves the idea of “having no limit” to the number of A’s he gives students.

“I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect.
 It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”

In addition to overreliance on AI, Garcia also pointed out that many students are underprepared mathematically, a concern echoed by campus associate teaching professor Gireeja Ranade.

Ranade noticed a similar lack of prerequisite mathematical skills in her spring 2026 EECS 127 class, “Optimization Models in Engineering,” which she described as “differently challenging” to teach this semester. The class saw a 16.8% F rate, far higher than the 5% of D’s and F’s that the EECS department describes as “typical” for an upper division course.

Ranade said students are expected to enter the course having taken classes on linear algebra, vector calculus and mathematical proofs. However, she found out in office hours that many students struggled with linear algebra, and was even more shocked when one student told her the linear algebra class they took at UC Berkeley had an “open-internet, open-AI policy” for homework and exams.

Both Garcia and Ranade have joined more than 1,300 UC faculty in signing a petition calling for the reinstatement of ACT and SAT standardized testing scores for STEM admissions in the UC system. The petition and its accompanying open letter detail similar concerns with students’ mathematical preparation.

Ranade also changed the structure of the course this year. Previously, EECS 127 included a final project completed with the guidance of the professor and a team of TAs. Due to a lack of staff, Ranade had to remove this portion of the class, on which she said most students get high scores.

According to a post on X by EECS department chair Jelani Nelson, the campus has had to reduce both undergraduate CS enrollment and the number of undergraduate TAs due to the high hourly wages that EECS TAs are paid.

Ranade and Garcia have both noticed the decline of student engagement in classes as well. Ranade said office hours used to be “overflowing,” but this semester, she and her TAs noticed “very low engagement” in office hours, despite frequently encouraging students to attend.

Garcia found a similar lack of attendance in his office hours over the past two semesters.

“I used to have full office hours, and for the first time, I was having nobody come to my office hours,” Garcia said. “It was just so surprising to sit in my office alone.”

Looking forward, both professors are rethinking their classes.

Garcia plans to “advertise” what happened in spring 2026 to his future classes on day one, while also trying to find a way to identify students who need extra remedial support.

Ranade emphasized that professors should be teaching students “more, not less,” in the age of AI, adding that she wants students to acquire critical thinking and analytical thinking skills necessary to become leaders to be “in a very competitive world.”

Both professors underscored the need for students to be more comfortable with difficult problems.

“We really need to make sure that we are preparing our students to be solid, contributing citizens and leaders — these are Berkeley students: not just next year or the year after, but for the next 40 years of their lives,” Ranade said. “We need to — and we want to — teach them how to … take on new challenges.”

“I love this phrase my colleague uses: ‘Confusion is the sweat of learning.’ I just love that,” Garcia said. “A lot of students, I think, are not putting in the sweat.”

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