Abstract
In this work, we propose a hybrid AI system consisting of a multi-agent system simulating students during an exam and a teacher monitoring them, as well as an evolutionary algorithm that finds classroom arrangements which minimize cheating incentives. The students will answer the exam based on how much knowledge they have about the topic of the exam. In our simulation, then they enter a decision phase in which, for those questions they don’t know the answer to, they will either cheat or answer by guessing. If a student gets caught cheating, his/her exam will be cancelled. The purpose of this study is to examine the question of how different monitoring behaviors on the part of the teacher affect the cheating behaviors of students. The results of this study show that an unbiased teacher, that is, a teacher that monitors every student with the same probability, produces minimal cheating incentives for students.
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This work has been supported by Asociación Mexicana de Cultura, A.C.
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Alazraki Salas, P.L., Gómez de Silva Garza, A. (2025). Finding Optimal Classroom Arrangements to Minimize Cheating in Exams Using a Hybrid AI System. In: Quintián, H., et al. Hybrid Artificial Intelligent Systems. HAIS 2024. Lecture Notes in Computer Science(), vol 14857. Springer, Cham. https://doi.org/10.1007/978-3-031-74183-8_11
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