Cargando…
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
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 possi...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449897/ https://www.ncbi.nlm.nih.gov/pubmed/37620342 http://dx.doi.org/10.1038/s41598-023-38964-3 |
_version_ | 1785095067083472896 |
---|---|
author | Ibrahim, Hazem Liu, Fengyuan Asim, Rohail Battu, Balaraju Benabderrahmane, Sidahmed Alhafni, Bashar Adnan, Wifag Alhanai, Tuka AlShebli, Bedoor Baghdadi, Riyadh Bélanger, Jocelyn J. Beretta, Elena Celik, Kemal Chaqfeh, Moumena Daqaq, Mohammed F. Bernoussi, Zaynab El Fougnie, Daryl Garcia de Soto, Borja Gandolfi, Alberto Gyorgy, Andras Habash, Nizar Harris, J. Andrew Kaufman, Aaron Kirousis, Lefteris Kocak, Korhan Lee, Kangsan Lee, Seungah S. Malik, Samreen Maniatakos, Michail Melcher, David Mourad, Azzam Park, Minsu Rasras, Mahmoud Reuben, Alicja Zantout, Dania Gleason, Nancy W. Makovi, Kinga Rahwan, Talal Zaki, Yasir |
author_facet | Ibrahim, Hazem Liu, Fengyuan Asim, Rohail Battu, Balaraju Benabderrahmane, Sidahmed Alhafni, Bashar Adnan, Wifag Alhanai, Tuka AlShebli, Bedoor Baghdadi, Riyadh Bélanger, Jocelyn J. Beretta, Elena Celik, Kemal Chaqfeh, Moumena Daqaq, Mohammed F. Bernoussi, Zaynab El Fougnie, Daryl Garcia de Soto, Borja Gandolfi, Alberto Gyorgy, Andras Habash, Nizar Harris, J. Andrew Kaufman, Aaron Kirousis, Lefteris Kocak, Korhan Lee, Kangsan Lee, Seungah S. Malik, Samreen Maniatakos, Michail Melcher, David Mourad, Azzam Park, Minsu Rasras, Mahmoud Reuben, Alicja Zantout, Dania Gleason, Nancy W. Makovi, Kinga Rahwan, Talal Zaki, Yasir |
author_sort | Ibrahim, Hazem |
collection | PubMed |
description | 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 ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students’ perspectives regarding the use of such tools in school work, and educators’ perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of 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 global 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 in school work. We find that ChatGPT’s performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT’s use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks. |
format | Online Article Text |
id | pubmed-10449897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104498972023-08-26 Perception, performance, and detectability of conversational artificial intelligence across 32 university courses Ibrahim, Hazem Liu, Fengyuan Asim, Rohail Battu, Balaraju Benabderrahmane, Sidahmed Alhafni, Bashar Adnan, Wifag Alhanai, Tuka AlShebli, Bedoor Baghdadi, Riyadh Bélanger, Jocelyn J. Beretta, Elena Celik, Kemal Chaqfeh, Moumena Daqaq, Mohammed F. Bernoussi, Zaynab El Fougnie, Daryl Garcia de Soto, Borja Gandolfi, Alberto Gyorgy, Andras Habash, Nizar Harris, J. Andrew Kaufman, Aaron Kirousis, Lefteris Kocak, Korhan Lee, Kangsan Lee, Seungah S. Malik, Samreen Maniatakos, Michail Melcher, David Mourad, Azzam Park, Minsu Rasras, Mahmoud Reuben, Alicja Zantout, Dania Gleason, Nancy W. Makovi, Kinga Rahwan, Talal Zaki, Yasir Sci Rep Article 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 ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students’ perspectives regarding the use of such tools in school work, and educators’ perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of 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 global 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 in school work. We find that ChatGPT’s performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT’s use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks. Nature Publishing Group UK 2023-08-24 /pmc/articles/PMC10449897/ /pubmed/37620342 http://dx.doi.org/10.1038/s41598-023-38964-3 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ibrahim, Hazem Liu, Fengyuan Asim, Rohail Battu, Balaraju Benabderrahmane, Sidahmed Alhafni, Bashar Adnan, Wifag Alhanai, Tuka AlShebli, Bedoor Baghdadi, Riyadh Bélanger, Jocelyn J. Beretta, Elena Celik, Kemal Chaqfeh, Moumena Daqaq, Mohammed F. Bernoussi, Zaynab El Fougnie, Daryl Garcia de Soto, Borja Gandolfi, Alberto Gyorgy, Andras Habash, Nizar Harris, J. Andrew Kaufman, Aaron Kirousis, Lefteris Kocak, Korhan Lee, Kangsan Lee, Seungah S. Malik, Samreen Maniatakos, Michail Melcher, David Mourad, Azzam Park, Minsu Rasras, Mahmoud Reuben, Alicja Zantout, Dania Gleason, Nancy W. Makovi, Kinga Rahwan, Talal Zaki, Yasir Perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
title | Perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
title_full | Perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
title_fullStr | Perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
title_full_unstemmed | Perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
title_short | Perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
title_sort | perception, performance, and detectability of conversational artificial intelligence across 32 university courses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449897/ https://www.ncbi.nlm.nih.gov/pubmed/37620342 http://dx.doi.org/10.1038/s41598-023-38964-3 |
work_keys_str_mv | AT ibrahimhazem perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT liufengyuan perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT asimrohail perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT battubalaraju perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT benabderrahmanesidahmed perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT alhafnibashar perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT adnanwifag perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT alhanaituka perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT alsheblibedoor perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT baghdadiriyadh perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT belangerjocelynj perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT berettaelena perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT celikkemal perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT chaqfehmoumena perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT daqaqmohammedf perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT bernoussizaynabel perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT fougniedaryl perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT garciadesotoborja perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT gandolfialberto perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT gyorgyandras perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT habashnizar perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT harrisjandrew perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT kaufmanaaron perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT kirousislefteris perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT kocakkorhan perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT leekangsan perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT leeseungahs perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT maliksamreen perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT maniatakosmichail perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT melcherdavid perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT mouradazzam perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT parkminsu perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT rasrasmahmoud perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT reubenalicja perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT zantoutdania perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT gleasonnancyw perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT makovikinga perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT rahwantalal perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses AT zakiyasir perceptionperformanceanddetectabilityofconversationalartificialintelligenceacross32universitycourses |