Enhancing Academic Integrity for Bangladesh's Educational Landscape

Abstract

Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must emphasize the cultivation of writing skills and critical thinking while guiding students to recognize the limitations of AI. While plagiarism checking is crucial for academic integrity, it often falls short in acknowledging students' originality. Natural Language Processing (NLP) emerges as a promising solution, augmenting plagiarism detection by comprehending context, nuances, and novel expressions. This paper proposed a model based on NLP that can incorporate a robust token identification mechanism into AI tools used by the students. A unique identification token is generated at the time of system login and stored in a publicly accessible resource, acting as a distinct marker for user activity. These identification tokens support a plagiarism detection system that continually reviews and compares activity records. It also enables a thorough examination of user involvement across various academic supplementary tools, such as academic writing, presentation creation, image generation, etc.