Four months ago, a Reddit user in r/uchicago described how professors delayed releasing fall quarter grades for Computer Science 141 due to “widespread plagiarism” on the final.
Directly underneath the post, a second user commented: “lmfao ig this happens every year now.”
CMSC 14100, colloquially known as CS 141, is an introductory course in the computer science (CS) department, and for nearly every quarter in the past few years, platforms like Sidechat and Reddit have been flooded with posts about artificial intelligence (AI) usage and what students are dubbing “cheating scandals.”
In the past few years, AI usage has increasingly become a concern for the course. Last fall, in addition to delayed grades, students taking CS 141 received a Canvas notification discussing academic dishonesty from Professors Adam Shaw, Blase Ur, Matthew Wachs, and Tamara Nelson-Fromm, the fall quarter instructors of the course.
The notification specifically cited low exam scores and a “high degree of use of ChatGPT and similar large language models (LLMs) in completing homework assignments… We are now rethinking both assessment and grading for this course, as well as for other courses we teach, including the upcoming iteration of CS142,” the professors wrote. Students were encouraged to self-report if they used AI.
“I think everyone knows that people are cheating. It’s kind of obvious,” said Jake Matarese, a second-year student who took the course autumn quarter of 2025. Matarese, like many of his peers, was not surprised by fall quarter grades being delayed due to academic dishonesty. “If everyone does really poorly on the exams and everyone does really well in the homework, that sort of suggests to us something’s going on.”
Last fall, the CS 141 final exam average was 79 out of 100 with a standard deviation of 17. However, available data does not include standard deviation and homework averages over time, making it hard to quantify whether there has been a large decline in exam grades relative to homework grades.
The professors declined to comment on the situation to the Maroon.
As changes continue to appear in the sequence, questions about academic dishonesty, whether AI use in the classroom can truly be mitigated, and the future of introductory computer science at UChicago have remained on the minds of students and faculty alike.
The 4-1-1 on 141
CMSC 14100 is part of a four-course introductory sequence required for computer science and related majors. Designed to teach basics of programming in Python, an introductory coding language, it is typically the first class that students who don’t test out of the requirement take.
“CS 141 is [a] pretty standard introductory course,” Matarese said, discussing the class’s workload. “You get one weekly homework, maybe about five to ten hours of work that focuses on the content of lectures.”
Matarese explained that the homework covers data structures such as lists, dictionaries, recursion, and basic functions, which he described as “very standard Python.”
For some students, however, the class has gained a reputation for its difficulty among those with little prior coding experience.
“Going into it, I was a little worried because I knew that, at least from what I heard, it was not an easy A,” said Michael Schneider, a second-year student who took 141 during the winter quarter of the 2024–25 academic year. Prior to taking the course, he had not taken a computer science class since early high school.
Additionally, CS 141 assignments are evaluated based on a SNU scale—Satisfactory (S), Needs Improvement (N), Unsatisfactory (U). An S indicates that the student completed an assignment and demonstrated sufficient mastery, an N indicates that the student completed an assignment but lacked sufficient mastery of material, and a U indicates that the student did not submit or complete a large portion of the work. This unique grading scale may be another reason why some beginners may have difficulty with the class.
According to Schneider, CS 141 gained a reputation for academic misconduct reports during the autumn quarter of the 2024–25 academic year, the quarter before he took the course. Like the incident that occurred this past fall, students received an email alleging widespread academic misconduct.
“It’s just so easy to just screenshot, GPT, copy, [and] paste,” he said, “there really can only be one right answer, so it’s easy not to get caught as well.”
Using an AI detector, such as Pangram, is one way to detect non-human written code. According to a Pangram article that discloses data on its ability to detect AI-generated code, AI-generated code is more difficult to detect than writing due to fewer stylistic choices available for developers compared to writers. The article also reported that Pangram’s AI detector has a 96.2 percent accuracy rate, a 0.3 percent false positive rate, and an 8.5 percent false negative rate. Accuracy can vary widely depending on the AI detector.
Even so, strategies to make AI generated code less obvious continue to circulate the internet on forums such as Reddit and Quora.
The computer science department is no stranger to cheating scandals, even prior to the 2024–25 autumn quarter. In 2018, Introduction to Computer Systems, CMSC 15400, released an Academic Dishonesty Policy requesting that students “do not attempt to find solutions online or from previous years.” Additionally, in 2020, the Maroon reported on a class-wide email alleging cheating via collaboration or copying from the internet in Computer Science with Applications I, CS 121.
Still, the autumn quarter of the 2024–25 academic year stands out among prior years’ cheating incidents, marking a shift toward AI-related academic misconduct. In the discussion post sent to students, AI usage was also cited as a major form of academic dishonesty during the incident last fall.
AI, Academic Misconduct, and the 2024 Autumn Quarter Incident
To understand the role that AI has had on cheating incidents in the Computer Science department, the Maroon spoke with professor Anne Rogers, a longtime professor of CS 141 who designed the introductory sequence and wrote the course’s textbook.
Rogers has dealt with academic dishonesty throughout her career. “It’s just part of the experience,” she said.
However, it also used to be easier to spot, according to Rogers. “[When] you get [answers] from your friend it’s so much easier to tell in that case, because there’s a certain amount of similarity that’s beyond random chance and you can say these clearly came from the same source.”
Now, with the prevalence of generative AI, she finds it harder to prove suspicion of academic dishonesty.
“We have more suspicions than we have concrete evidence,” said Rogers. “Where a student turns in code that is far more advanced than anything we’ve taught, you can be pretty sure they used a tool, unless they can explain where they learned those concepts. If they turn in code that’s kind of vanilla, it’s really, really hard to tell.”
During the 2024–25 autumn quarter, Rogers sent an email to her students during the reading period that accused a large portion of using AI. In the email, she gave each student a chance to self-report. Those who chose to self-report were marked down by one letter grade for each homework assignment they reported using AI on. Those who didn’t self-report, but were suspected of using AI, were subject to a formal plagiarism accusation, which could result in failing the class and further disciplinary actions from the University.
“We wanted to give people a chance to say, ‘You know what? I really don’t want to be that person,’” she said.
“Sometimes they’ll just admit it and then you can have a conversation about why this is bad for their education,” Rogers added. “To me, the penalty is part of the process, and without it, there would be no hope at all.”
Rogers said that many students decided to self-report.
“Right before finals, she sent out an email and it [said], ‘If you used AI, we know. So either self-report or get called to the academic [honesty] office, and get an F,’” said H.R., an anonymous student who took the class that quarter, and chose to self-report when she received the notification.
H.R. explained that she didn’t understand the extent to which AI was acceptable. “It was my first quarter so I didn’t really know what to expect with AI, and I didn’t know how strict it was in college,” H.R. said. “I was just using it, like everyone does.”
CS 141’s fall quarter syllabus states that the use of ChatGPT, GitHub CoPilot and other AI tools were prohibited and that students found in violation of the academic honest policy would face severe effects on their grade.
H.R. received a full letter grade penalty for each homework she reported as completed with the use of AI.
According to Rogers, the computer science faculty use Gradescope’s Measure of Software Similarity (MOSS) tool to identify potential AI usage. MOSS flags any submissions of concern, then, professors will comb through suspicious activity by hand to eliminate false positives before confronting a student about academic dishonesty.
The process of identifying students who used AI dragged on for weeks into the following quarter, Rogers said.
“I think students don’t appreciate how truly soul-crushing having to deal with academic dishonesty is for instructors,” she said. As of the current academic year, she no longer teaches CS 141.
CS Professors “Know AI”
AI related cheating extends beyond the CS department at the University; however, it may appear to be contained to CS 141 due to how frequently the class is discussed on campus.
Matarese suggested that cheating in CS 141 may appear to occur more frequently in part due to the makeup of the students taking the class being a mix of computer science majors and non-majors: “Some kids, who might have to take [CS 141] for one reason or another, but they don’t really [care] about comp-sci, maybe they’re better incentivized to cheat.”
Fourth-year Jillian Hooey, who took the course last quarter, echoed Matarese’s sentiment.
“This is going to be one of those classes where I just cram, and then regurgitate, and then move on,” Hooey said.
AI may also appear more prevalent in computer science classes because of the wealth of available evidence—or at least the lack of plausible deniability. Fourth-year Jessie Wang, a co-student advocate for the Student Advocacy Office (SAO) explained that there has been a noticeable difference in many cases involving plagiarism since she initially joined the SAO three years ago. The SAO is an independent branch of the Undergraduate Student Government, offering support to students through conduct, housing and financial-aid processes.
“There’s absolutely been an increase in AI cases, I think, since more people became aware of or started using ChatGPT, or the technology itself started improving,” Wang said.
When gathering materials to present to the disciplinary board, AI-related cases involving essays are easier to dispute because of a student’s ability to show a document history, Wang said.
“Coding assignments are difficult [to dispute] a lot of the time,” she said. She suggested that this is in part due to Computer Science professors’ familiarity with AI tools.
“[CS professors] can pick [AI] out with higher accuracy or they have other code that can pick that out. Whereas sometimes social [science] professors aren’t quite sure what AI writing looks like, so they might wrongly accuse a student.”
Materese believes that while AI is used in all programs of study, when it’s used for writing it’s harder to tell what is and isn’t student produced: “I know some people who have used AI for writing and they haven’t gotten caught. I know some people who didn’t use AI, and they got flagged for AI.”
“[CS professors] know AI, they research AI, they do all these things with AI, so they’re more comfortable with it than your old, 70-year-old SOSC teacher,” he added.
The Industry’s Dilemma
According to Stanford’s 2025 AI Index Report, private investment in AI grew $109.1 billion in 2024 in the U.S., with 78 percent of organizations reporting AI usage that same year. The index also reports that 81 percent of K-12 computer science teachers believe AI, “should be part of foundational CS education.”
As AI tools become increasingly prevalent, the acceptable boundaries of AI usage at UChicago continue to shift, particularly because the University does not have a centralized AI policy and usage of AI tools varies on a class-by-class basis, though last summer it released a report on AI use in education.
“I can’t imagine a policy that would be appropriate for both 141, where we’re trying to teach people foundational material … versus a class for fourth years, where they’ve already built those skills,” said Rogers.
For computer science courses, this has created challenges for both professors and students regarding the acceptable limits of AI usage.
“I think AI can take away hundreds and thousands of hours of you slaving away trying to find that one bug in your code,” Schneider, who took CS 141 last year, said. “And why not? AI can find [bugs] within 10 minutes, and for me, it would take a team of 10 to do it.”
“In the short term it’s rough, because in these introductory courses, people are getting away with cheating,” he added. “[But] in the long term, if I’m trying to program a website, or a game, or something like that, having something that can take away hours of manual labor is extremely valuable.”
Some industries reflect an increased usage of AI generated code. According to a CNBC article, as much as 30 percent of Microsoft code is written by AI. At Meta, 65 percent of engineers are expected to write 75 percent of their code using AI, according to a Business Insider article.
According to Rogers, the CS department has been grappling with ensuring the building of foundational skills without leaving students unprepared to work with AI, since it’s now the industry standard. The skills required of software engineers and programmers are evolving to include more AI-assisted tasks, such as system design and higher-level problem solving.
“Every class is going to have a different take on this,” Rogers said. She added that some professors teaching upper-level CS classes allow for, or even encourage, AI usage depending on the subject matter and context: “I want my colleagues to be able to do that if they think that’s appropriate in their classes.”
For introductory courses, however, Rogers believes a “no AI policy” is still important. “In the foundational classes, we really think it gets in the way of students actually learning anything.”
Since the cheating incident that occurred in the fall quarter, students now face the department’s new efforts to curb AI usage; Hooey says lectures are now accompanied by a no-screen policy on top of the already established in-person, handwritten tests.
“I feel like that’s such a funny dichotomy,” Hooey said.
CS 141’s 2025–26 fall quarter incident marks the second year that AI-related cheating scandals have occurred in the course. The rapid changes that AI has introduced to the field of computer science has created a dilemma for students and staff campus wide. There is no consensus on the extent to which AI should or shouldn’t be used in the classroom to best train the next generation of computer scientists.
“If you let the kids use AI, then you’re turning out less qualified computer scientists, and then the name of the school is tarnished,” Materese said.
“I’d say they have a responsibility to [report students],” he added. “How they can do it without dogging the kids all the time, I don’t know.”
