Reacting to Generative AI: Insights from Student and Faculty Discussions on Reddit
Open accessAbstract
Generative Artificial intelligence (GenAI) such as ChatGPT has elicited strong reactions from almost all stakeholders across the education system. Education-oriented and academic social media communities provide an important venue for these stakeholders to share experiences and exchange ideas about GenAI, which is constructive for developing human-centered policies. This study examines early user reactions to GenAI, consisting of 725 Reddit threads between 06/2022 and 05/2023. Through natural language processing (NLP) and content analysis, we observe an increasingly negative sentiment in the discussion and identify six main categories of student and faculty experiences of GenAI in education. These experiences reflect concerns about academic integrity and AI's negative impact on the value of traditional education. Our analysis also highlights an additional workload imposed by new technologies. Our findings suggest that dialogue between stakeholders in the education community is critical and can mitigate sources of tension between students and faculty.
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Faculty could complain about increased workload due to the need to manage more AI-related academic integrity problems.
“Recent research has shown that faculty could complain about the increased workload due to the need to manage more AI-related academic integrity problems [62].”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [62]
Identifying inappropriate use of AI in coursework is a known struggle for faculty and instructors.
“For example, identifying inappropriate use of AI in coursework is a known struggle for faculty and instructors [62]”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [62]
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Cites
Determining Validity in Qualitative Inquiry
Creswell, J. W., & Miller, D. L. (2000) · Theory Into Practice · doi:10.1207/s15430421tip3903_2 · Cited by 9,184 · see citing works
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · see citing works
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · see citing works
A comprehensive AI policy education framework for university teaching and learning
Chan, C. K. Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00408-3 · Cited by 1,577 · see citing works
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
Grootendorst, M. (2022) · arXiv · doi:10.48550/arxiv.2203.05794 · Cited by 1,423 · see citing works
Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839 · see citing works
What ChatGPT and generative AI mean for science
Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828 · see citing works
ChatGPT: The End of Online Exam Integrity?
Susnjak, T. (2022) · arXiv · doi:10.48550/arxiv.2212.09292 · Cited by 370 · see citing works
Will ChatGPT get you caught? Rethinking of Plagiarism DetectionPreprint
Khalil, M., & Er, E. (2023) · Interacción · doi:10.48550/arxiv.2302.04335 · Cited by 369 · see citing works
ChatGPT is not all you need. A state-of-the-art review of large generative AI models
Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023) · arXiv · doi:10.48550/arxiv.2301.04655 · Cited by 245 · see citing works
Mitrović, S., Andreoletti, D., & Ayoub, O. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.13852 · Cited by 78 · see citing works
Seeing ChatGPT Through Students' Eyes: An Analysis of TikTok Data
Haensch, A.-C., Ball, S., Herklotz, M., & Kreuter, F. (2023) · arXiv · doi:10.48550/arxiv.2303.05349 · Cited by 62 · see citing works
On the Possibilities of AI-Generated Text Detection
Chakraborty, S., Bedi, A. S., Zhu, S., An, B., Manocha, D., & Huang, F. (2023) · arXiv · doi:10.48550/arxiv.2304.04736 · Cited by 59 · see citing works
Perceptions About Generative AI and ChatGPT Use by Faculty and College Students
Petricini, T., Wu, C., & Zipf, S. T. (2023) · doi:10.35542/osf.io/jyma4 · Cited by 54 · see citing works
Self-citationChatGPT in education: A discourse analysis of worries and concerns on social mediaPreprint
Li, L., Ma, Z., Fan, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.02201 · Cited by 25 · see citing works
Exploring the Efficacy of ChatGPT in Analyzing Student Teamwork Feedback with an Existing TaxonomyPreprint
Katz, A., Wei, S., Nanda, G., Brinton, C. G., & Ohland, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.11882 · Cited by 16 · see citing works
Flagged first, then by citations
Research works printed but not linked: 40
- Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 54-59.
- Ashraf Alam. 2022. Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: reforming education in the age of artificial intelligence. In Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2022. Springer, 395-406.
- Carl Auerbach and Louise B Silverstein. 2003. Qualitative data: An introduction to coding and analysis. Vol. 21. NYU press.
- David Baidoo-Anu and Leticia Owusu Ansah. 2023. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI 7, 1 (2023), 52-62.
- Cara Brems, Martina Temmerman, Todd Graham, and Marcel Broersma. 2017. Personal branding on Twitter: How employed and freelance journalists stage themselves on social media. Digital journalism 5, 4 (2017), 443-459.
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Pra- fulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, and Amanda Askell. 2020. Language Models Are Few-Shot Learners. Advances in neural information processing systems 33 (2020), 1877-1901.
- Wonchan Choi, Yan Zhang, and Besiki Stvilia. 2023. Exploring Applications and User Experience with Generative AI Tools: A Content Analysis of Reddit Posts on ChatGPT. Proceedings of the Association for Information Science and Technology 60, 1 (2023), 543-546.
- Clayton M Christensen and Henry J Eyring. 2011. The innovative university: Changing the DNA of higher education from the inside out. John Wiley & Sons.
- Nassim Dehouche. 2021. Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). Ethics in Science and Environmental Politics 21 (2021), 17-23.
- Xuefan Dong and Ying Lian. 2021. A review of social media-based public opinion analyses: Challenges and recommendations. Technology in Society 67 (2021), 101724.
- Andy Extance. 2023. ChatGPT has entered the classroom: how LLMs could transform education. Nature 623, 7987 (2023), 474-477.
- Yunhe Feng, Pradhyumna Poralla, Swagatika Dash, Kaicheng Li, Vrushabh Desai, and Meikang Qiu. 2023. The impact of chatgpt on streaming media: A crowd- sourced and data-driven analysis using twitter and reddit. In 2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 222-227.
- José-María Fernández-Batanero, Pedro Román-Graván, Miguel-María Reyes- Rebollo, and Marta Montenegro-Rueda. 2021. Impact of educational technology on teacher stress and anxiety: A literature review. International journal of envi- ronmental research and public health 18, 2 (2021), 548.
- Barbara Silveira Fraga, Ana Paula Couto da Silva, and Fabricio Murai. 2018. Online social networks in health care: a study of mental disorders on Reddit. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 568-573.
- Catherine A Gao, Frederick M Howard, Nikolay S Markov, Emma C Dyer, Siddhi Ramesh, Yuan Luo, and Alexander T Pearson. 2022. Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. BioRxiv (2022), 2022-12.
- Simone Grassini. 2023. Shaping the future of education: exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences 13, 7 (2023), 692.
- Farrokh Habibzadeh. 2023. GPTZero performance in identifying artificial intelligence-generated medical texts: a preliminary study. Journal of Korean Medical Science 38, 38 (2023).
- Jari J Hakanen, Arnold B Bakker, and Wilmar B Schaufeli. 2006. Burnout and work engagement among teachers. Journal of school psychology 43, 6 (2006), 495-513.
- Mubin Ul Haque, Isuru Dharmadasa, Zarrin Tasnim Sworna, Roshan Namal Rajapakse, and Hussain Ahmad. 2022. " I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. arXiv preprint arXiv:2212.05856 (2022). · arXiv
- Sue Hrasky and David Kronenberg. 2011. Curriculum redesign as a faculty- centred approach to plagiarism reduction. International Journal for Educational Integrity 7, 2 (2011).
- Hui-Wen Huang, Zehui Li, and Linda Taylor. 2020. The Effectiveness of Us- ing Grammarly to Improve Students' Writing Skills. In Proceedings of the 5th International Conference on Distance Education and Learning. 122-127.
- Eva Jereb, Matjaž Perc, Barbara Lämmlein, Janja Jerebic, Marko Urh, Iztok Podbre- gar, and Polona Šprajc. 2018. Factors influencing plagiarism in higher education: A comparison of German and Slovene students. PloS one 13, 8 (2018), e0202252.
- Georgios Lampropoulos, Richard E Ferdig, and Regina Kaplan-Rakowski. 2023. A Social Media Data Analysis of General and Educational Use of ChatGPT: Understanding Emotional Educators. Available at SSRN 4468181 (2023).
- Xiao Ma, Jeff Hancock, and Mor Naaman. 2016. Anonymity, intimacy and self- disclosure in social media. In Proceedings of the 2016 CHI conference on human factors in computing systems. 3857-3869.
- Ehsan Mohammadi, Mike Thelwall, Mary Kwasny, and Kristi L Holmes. 2018. Academic information on Twitter: A user survey. PloS one 13, 5 (2018), e0197265.
- Junaid Qadir. 2023. Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON). IEEE, 1-9.
- Md Mostafizer Rahman and Yutaka Watanobe. 2023. ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences 13, 9 (2023), 5783.
- Matthew Rowe and Harith Alani. 2014. Mining and comparing engagement dynamics across multiple social media platforms. In Proceedings of the 2014 ACM conference on Web science. 229-238.
- Jürgen Rudolph, Samson Tan, and Shannon Tan. 2023. ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching 6, 1 (2023).
- Ben Shneiderman. 2022. Human-centered AI. Oxford University Press.
- Emmanuel Sibomana, Irenee Ndayambaje, and Emmanuel Uwambayinema. 2018. Plagiarism in higher education environment: Causes and solutions. Rwandan journal of education 4, 2 (2018), 15-23.
- Harpreet Singh, Mohammad-Hassan Tayarani-Najaran, and Muhammad Yaqoob. 2023. Exploring Computer Science Students' Perception of ChatGPT in Higher Education: A Descriptive and Correlation Study. Education Sciences 13, 9 (2023), 924.
- Jan Smits and Tijn Borghuis. 2022. Generative AI and Intellectual Property Rights. In Law and Artificial Intelligence: Regulating AI and Applying AI in Legal Practice. Springer, 323-344.
- Sarin Sok and Kimkong Heng. 2023. ChatGPT for education and research: A review of benefits and risks. Available at SSRN 4378735 (2023).
- Shaina J Sowles, Melissa J Krauss, Lewam Gebremedhn, and Patricia A Cavazos- Rehg. 2017. "I feel like I've hit the bottom and have no idea what to do": Supportive social networking on Reddit for individuals with a desire to quit cannabis use. Substance abuse 38, 4 (2017), 477-482.
- Meng-Lin Tsai, Chong Wei Ong, and Cheng-Liang Chen. 2023. Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT. Education for Chemical Engineers 44 (2023), 71-95.
- Sanna Vehviläinen, Erika Löfström, and Anne Nevgi. 2018. Dealing with pla- giarism in the academic community: Emotional engagement and moral distress. Higher Education 75 (2018), 1-18.
- Hengki Wijaya and Kara Elizabeth Gruber. 2018. Ethics perspective and regulation of plagiarism in Higher Education. International Journal of Humanities and Innovation (IJHI) 1, 1 (2018), 17-25.
- Bin Zhang, Haibin Xia, and Hun-ok Lim. 2019. Development of an AI based teach- ing assisting system. In Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing. 1-5.
- Wenkai Zhou, Chi Zhang, Linwan Wu, and Meghana Shashidhar. 2023. ChatGPT and marketing: Analyzing public discourse in early Twitter posts. Journal of Marketing Analytics (2023), 1-14.
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AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
Wu, C., Zhang, H., & Carroll, J. M. (2024) · Future Internet · doi:10.3390/fi16100354 · Cited by 99 · see citing works
Self-citationChoi, W., Bak, H., An, J., Zhang, Y., & Stvilia, B. (2025) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24978 · Cited by 27 · see citing works
- Authors
- Wu, C., Wang, X., Carroll, J., & Rajtmajer, S.
- Affiliations
- Pennsylvania State University (US)
- Venue
- ACM Web Science Conference (WebSci)
- Publisher
- Center for Open Science
- Published
- 2024-01-18
- Pages
- 103-113
- OpenAlex
- W4394879862
- Cited by
- 26
- References
- 16
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 3
- FWCI
- 9.88
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- ACM Web Science Conference (WebSci) (publishedVersion)
- EdArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI in education, ChatGPT, student and faculty perceptions, Reddit discussions, academic integrity, AI policy in higher education, stakeholder conflict
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