English

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

Computers and Society 2023-08-30 v1 Artificial Intelligence

Abstract

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work -- a possibility that has sparked discussions on the integrity of student evaluations in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses. Further, students' perspectives regarding the use of such tools, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of ChatGPT against students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use. We find that ChatGPT's performance is comparable, if not superior, to that of students in many courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection. Finally, we find an emerging consensus among students to use the tool, and among educators to treat this as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of AI into educational frameworks.

Keywords

Cite

@article{arxiv.2305.13934,
  title  = {Perception, performance, and detectability of conversational artificial intelligence across 32 university courses},
  author = {Hazem Ibrahim and Fengyuan Liu and Rohail Asim and Balaraju Battu and Sidahmed Benabderrahmane and Bashar Alhafni and Wifag Adnan and Tuka Alhanai and Bedoor AlShebli and Riyadh Baghdadi and Jocelyn J. Bélanger and Elena Beretta and Kemal Celik and Moumena Chaqfeh and Mohammed F. Daqaq and Zaynab El Bernoussi and Daryl Fougnie and Borja Garcia de Soto and Alberto Gandolfi and Andras Gyorgy and Nizar Habash and J. Andrew Harris and Aaron Kaufman and Lefteris Kirousis and Korhan Kocak and Kangsan Lee and Seungah S. Lee and Samreen Malik and Michail Maniatakos and David Melcher and Azzam Mourad and Minsu Park and Mahmoud Rasras and Alicja Reuben and Dania Zantout and Nancy W. Gleason and Kinga Makovi and Talal Rahwan and Yasir Zaki},
  journal= {arXiv preprint arXiv:2305.13934},
  year   = {2023}
}

Comments

17 pages, 4 figures