Kostroma, Kostroma, Russian Federation
JEL O33 Technological Change: Choices and Consequences • Diffusion Processes
JEL K42 Illegal Behavior and the Enforcement of Law
The rapid integration of artificial intelligence technologies in many areas of life has expanded opportunities for successful student fraud in the educational process. Based on an analysis of recent literature, this article discusses two types of measures aimed at preventing and reducing cases of AI plagiarism. The first type, referred to as punitive, is based on principles from economic theory of crime and punishment and involves increased control and stricter penalties. The second type consists of preventive and educational measures that focus on shaping students’ value systems and promoting academic integrity along with ethical use of AI tools. Punitive measures cannot eliminate the root causes of academic misconduct but serve primarily to signal potential offenders about the unprofitability of violations. Preventive measures, unlike punitive ones, target underlying reasons but do not promise quick results. Therefore, an optimal strategy can only be built through a comprehensive approach combining both methods. Numerous surveys indicate that most Russian students generally see nothing wrong with cheating or copying work. From early childhood, they are accustomed to having their school assignments completed by parents or grandparents. Thus, effectively curbing the scale of academic fraud among students without resorting to harsh punitive measures seems impossible at present. However, applying such disciplinary actions against violators – such as giving failing grades or expulsion from university – is financially impractical for many Russian universities under per-student funding models. Consequently, there is reason to believe that many provincial universities in Russia will struggle to address the issue of student academic fraud effectively.
artificial intelligence, education, cheating, plagiarism, code of ethics, academic fraud
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