# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 5 Tools and Strategies for Ensuring Academic Integrity
### Plagiarism Detection Methods
#### 5.3.2 Automated Detection Software
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Purpose** & **Code Lines** & **Origin** \\ \hline \multirow{2}{*}{\begin{tabular}{l} Merge three dictionaries into one by combining values \\ into tuples if keys overlap. \\ \end{tabular} } & 15 & ChatGPT \\ \cline{2-3}  & 22 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Determine the middle two numbers from four numeric \\ inputs. \\ \end{tabular} } & 17 & ChatGPT \\ \cline{2-3}  & 33 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Count different data types in a list, prompted by user \\ input. \\ \end{tabular} } & 16 & ChatGPT \\ \cline{2-3}  & 31 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Calculate and display Penta numbers within a given \\ range. \\ \end{tabular} } & 24 & ChatGPT \\ \cline{2-3}  & 15 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Functionally manipulate and transform lists of lists \\ based on user selection. \\ \end{tabular} } & 27 & ChatGPT \\ \cline{2-3}  & 44 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Generate a random poem with a specified number of \\ lines, using a modified tuple structure. \\ \end{tabular} } & 18 & ChatGPT \\ \cline{2-3}  & 31 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Perform left and right bitwise shifts on binary numbers, \\ including circular shifts. \\ \end{tabular} } & 16 & ChatGPT \\ \cline{2-3}  & 20 & Human \\ \hline \multirow{2}{*}{\begin{tabular}{l} Classify data using the K-nearest neighbors algorithm \\ with Euclidean distance. \\ \end{tabular} } & 84 & ChatGPT \\ \cline{2-3}  & 87 & Human \\ \hline \multirow{2}{*}{\
\begin{tabular}{l} Evaluate a Connect-4 board position with a heuristic \\ function to assess the board state. \\ \end{tabular} } & 100 & ChatGPT \\ \cline{2-3}  & 141 & Human \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Perform k-means clustering on building data to analyze \\ neighborhood statistics. \\ \end{tabular} } & 53 & ChatGPT \\ \cline{2-3}  & 78 & Human \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Code analysis. that some of the software returned percentages for their classifications. In such instances, any category (either human or AI) with a percentage greater than 50% was considered as AI-generated for the final classification. The performance of the tools evaluated in this study exhibited significant variability. Copyeleaks demonstrated a robust ability to identify all AI-generated code; however, it incorrectly classified 50% of the human-written solutions. Conversely, tools such as Content at Scale and Writer were proficient in detecting human-written code but were entirely unsuccessful in identifying AI-generated code. SEO.ai and Corrector delivered mixed outcomes, with Corrector in particular being prone to frequent crashes when analyzing longer inputs. Notably, Copyeleaks emerged as the most effective tool, achieving an accuracy rate of 75%. In contrast, the majority of the methods evaluated performed no better than random guessing, which would theoretically yield an accuracy of 50%. These findings lead to the conclusion that the current suite of automated tools remains insufficient for reliably determining the origin of GenAI-generated code in an academic setting. Given the limitations of automated AI detection tools, universities have issued diverse guidelines on their use. The University of Pretoria recommends strategies such as on-campus invigilated assessments for exams, the use of tools such as Turnitin for detecting AI-generated plagiarism in asynchronous assignments, and Proctorio for real-time online proctoring. Additionally, the university advocates for authentic assessments, including projects and case studies, in order to mitigate academic dishnesty. Columbia University advises a cautious approach, emphasizing the risks of misidentification and advocating for transparency with students. It underscores that AI detection tools should supplement--rather than dictate--grading decisions.