Academic integrity is fundamental to the credibility and quality of educational institutions. However, academic misconduct, including plagiarism, cheating, and fabrication of data, undermines this integrity. Traditional methods of detecting academic misconduct rely heavily on manual reviews and self-reporting, which are often time-consuming and prone to oversight. This research explores the application of data science techniques to quantitatively identify patterns in academic misconduct. By analyzing large datasets from academic institutions, including student records, assignment submissions, and digital interactions, we employ machine learning algorithms and statistical models to detect anomalies and predict potential misconduct. Our findings demonstrate that data-driven approaches significantly enhance the accuracy and efficiency of identifying academic misconduct, offering a scalable solution for maintaining academic integrity. This study provides a framework for integrating data science into academic integrity initiatives, highlighting the potential for proactive and preventative measures against academic misconduct.

Academic Integrity, Data Science, Academic Misconduct Detection, Predictive Modeling, Anomaly Detection.