Abstract:

This research project develops a system for automatically detecting cheating and identifying students in order to improve exam integrity while addressing the shortcomings of traditional monitoring methods. The technology detects and captures cheating pupils in real time using both machine learning and manual tactics. A study and analysis were conducted to provide evidence-based recommendations for designing effective automated cheating detection systems in educational settings. According to the PICOS framework, the research is aimed at students who struggle with exam cheating (Population), focuses on developing a detection system (Intervention), compares traditional monitoring techniques to the new system (Comparison), seeks to improve accuracy and fairness in identifying cheating (Outcome), and collects evidence using systematic review and meta-analysis methods (Study Design). The literature search followed PRISMA criteria and includes papers from the Scopus and Google Scholar databases from 2013 to 2024. The inclusion criteria included research papers that investigated exam participants, instances of cheating, and the application of new technologies such as deep learning and machine learning. Articles that were not about examination malpractices or did not use advanced technological tools were rejected based on particular criteria. A total of 37 articles were reviewed. The findings demonstrate how new technology may significantly increase the credibility and dependability of tests, ensuring academic honesty.