Perception, performance, and detectability of conversational artificial intelligence across 32 university courses

Project Overview

The document explores the role of generative AI, especially ChatGPT, in education, underscoring its ability to generate human-like text and the associated challenges to academic integrity. A comparative study across 32 university-level courses indicates that ChatGPT's performance often matches or exceeds that of students, raising concerns about the implications for learning and assessment. Furthermore, the text addresses the complexities of identifying AI-generated content, which complicates the enforcement of academic standards. It also reflects on the varying attitudes of students and educators towards the integration of AI tools in academic work, suggesting a need for a thoughtful approach to harnessing the benefits of generative AI while mitigating risks. Overall, the findings highlight the transformative potential of generative AI in education and the necessity for ongoing dialogue around its ethical and practical use in learning environments.

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Authors: Hazem Ibrahim, Fengyuan Liu, Rohail Asim, Balaraju Battu, Sidahmed Benabderrahmane, Bashar Alhafni, Wifag Adnan, Tuka Alhanai, Bedoor AlShebli, Riyadh Baghdadi, Jocelyn J. Bélanger, Elena Beretta, Kemal Celik, Moumena Chaqfeh, Mohammed F. Daqaq, Zaynab El Bernoussi, Daryl Fougnie, Borja Garcia de Soto, Alberto Gandolfi, Andras Gyorgy, Nizar Habash, J. Andrew Harris, Aaron Kaufman, Lefteris Kirousis, Korhan Kocak, Kangsan Lee, Seungah S. Lee, Samreen Malik, Michail Maniatakos, David Melcher, Azzam Mourad, Minsu Park, Mahmoud Rasras, Alicja Reuben, Dania Zantout, Nancy W. Gleason, Kinga Makovi, Talal Rahwan, Yasir Zaki

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