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17 cases of academic dishonesty were caught this week.

Out of 23 checked. Another 40 matched a classmate's work. The latest three:

Csubmission.h 77%
10struct FQPProjectileParams11{12 FFloatPoint Location;13 FFloatPoint Direction;14 float Damage = 0.f;15 float Speed = 0.f;16 17 std::shared_ptr<FTexture> Texture = nullptr;18 std::string TargetTag;

12 lines copied.

Found on the web 6 days ago
Pythonsubmission.py 95%
22def __init__( 23 self, 24 num_states=100,25 num_actions=4, 26 alpha=0.2, 27 gamma=0.9, 28 rar=0.5, 29 radr=0.99, 30 dyna=0,

44 lines copied word for word.

Found on the web 6 days ago
C++submission.cpp 87%
6 7Archive.AddData(, DifficultyInterval);8 9Archive.AddData(, ScorePerKill);10Archive.AddData(, ScorePerLevel);11Archive.AddData(, ScorePerLevelIncrease);12 13Archive.AddData(, PlayerRespawnTime);14Archive.AddData(, PlayerRespawnTimePerLevel);

8 lines copied.

Found on the web 6 days ago

Student names, comments and strings are removed before anything is shown here.

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Line-by-line evidence

Every match, traced
to its source

Codequiry reads each submission line by line and shows exactly which lines match the open web, GitHub, AI-written code or another student in the course, each with its source and a similarity score.

cs-105 · lab_07 / wordle_solver.py 3 flagged sections
1# CS-105 Lab 7 jpatel42
2import random # for picking starter words later
3 
4WORDS = open("words.txt").read().split()
5# print(len(WORDS)) # debug - should be ~12k
6 
7def score_guess(guess, answer):Lines 7–17 · Chegg · 97%
8 feedback = ["miss"] * 5
9 answer_chars = list(answer)
10 for i in range(5):
11 if guess[i] == answer[i]:
12 feedback[i] = "hit"
13 answer_chars[i] = None
14 for i in range(5):
15 if feedback[i] == "miss" and guess[i] in answer_chars:
16 feedback[i] = "close"
17 answer_chars.remove(guess[i])
18 return feedback
19 
20# TODO: cache this between rounds??
21def filter_words(words, guess, feedback):Lines 21–25 · GitHub · 94%
22 return [w for w in words if score_guess(guess, w) == feedback]
23 
24def letter_frequency(words):
25 counts = {}
28 return counts
29 
30from typing import List, DictLines 30–41 · AI-written · 89%
31 
32def best_next_guess(candidates: List[str]) -> str:
33 """Select the most informative next guess.
34 Args:
35 candidates: A list of remaining valid 5-letter words.
36 Returns:
37 The candidate that maximizes letter-frequency coverage.
38 """
39 freq: Dict[str, int] = letter_frequency(candidates)
40 score = lambda w: sum(freq.get(c, 0) for c in set(w))
41 return max(candidates, key=score)
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Inside the report

Find plagiarism matches across every code source

Each submission is compared with the open web, public repositories and previous cohorts, and screened for AI-written code. The matched lines are set out side by side, ready for your review.

AI-written89%
Web & repo match92%
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AI detection analysis showing a code block flagged as AI-written with probability score and signals
Submissions dashboard listing student files with peer, web, and AI plagiarism scores per submission
Detection tools configuration with web, GitHub, Stack Overflow, Gist, Software Heritage, and AI detection sources
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Check code for plagiarism
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Cluster graphs, match tables and score distributions show where to look first. Open any submission to see its matches highlighted and compared side by side.

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Four checks, one report

  • Peer comparison

    Every submission against every other in the course. Renamed variables and reordered functions still match.

  • Web sources

    GitHub, Stack Overflow and Chegg, with a link to each source.

  • AI detection

    How likely each file is to be written by ChatGPT, Claude or Copilot.

  • Evidence summary

    A risk score for each submission. The determination stays with you.

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Feature
Codequiry
MOSS
Dolos
Peer comparison Submissions compared with one another
Web source matching GitHub, Stack Overflow and the open web
AI code detection Code written by ChatGPT or Copilot
Structural analysis Matches code after renaming and reordering
Visual report Side-by-side evidence in the browser
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In use at 500+ institutions worldwide

"Teaching over 500 students per term, we faced a critical challenge: detecting code copied from ChatGPT and online sources without viable tools. Codequiry enables us to batch-process entire cohorts efficiently. This technology is essential for maintaining rigorous standards in computer science education."

MIT Senior Lecturer

"The rise of AI-generated code has made verifying student mastery a critical challenge. Codequiry is now essential for upholding our academic standards and ensuring every submission represents original effort."

Stanford University Professor

"Great way to protect your course from plagiarised work"

Georgia Tech Lecturer

"Makes it easy to see when a submission was copied from Chegg."

University of Toronto Professor

"Catches plagiarism when students are submitting copied work"

Carnegie Mellon Professor

"Love that I can exclude my starter code. Used to get tons of false matches because everyone had the same template."

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"Been using it 2 years now. Solid. Does what it says."

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