Higher Education Faculty Perceptions of ChatGPT and the Influencing Factors: A Sentiment Analysis of X
Open accessAbstract
ChatGPT, an AI chatbot developed by OpenAI, was released in November 2022, sparking a significant surge in global awareness and utilization of generative AI across various domains. Although recent studies have acknowledged the significance of ChatGPT in the education sector, they have yet to focus on exploring faculty attitudes toward ChatGPT. We gathered a comprehensive corpus of tweets containing “#ChatGPT” and “#highered” between November 30th, 2022, and April 30th, 2023. We analyzed data by triangulating VADER, NRC lexicon, and ground coding. Findings suggest that 40% of the expressed sentiments were positive, 51% were neutral, and 9% were negative. The study also revealed the diverse range of emotions held by higher education faculty regarding ChatGPT, with trust and joy being the most prevalent positive sentiments and fear and anger being the most prevalent negative sentiments. This study shed light on faculty members’ perceptions of ChatGPT, contributing to a better understanding of the impact, emotions, and incorporation of ChatGPT in the higher education sector.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The experience of mixed emotional responses and ethical navigation among Christian faculty was supported by literature.
“The experience of mixed emotional responses and ethnical navigation was supported by literature (Hagendorff, 2020;Mamo et al., 2024;Mittelstadt, 2019).”
From Into the Unknown: AI Adoption and Job Satisfaction of Christian Faculty (2026) · cites this paper as Mamo et al., 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 2× · see citing works
“Typically, LLMs rely heavily on training data, and when the data contain biases or anomalies, it could lead to unfair or biased results (Kasneci et al., 2023).”
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works
Intercoder Reliability in Qualitative Research: Debates and Practical Guidelines
O’Connor, C., & Joffe, H. (2020) · International Journal of Qualitative Methods · doi:10.1177/1609406919899220 · Cited by 3,871 · see citing works
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297 · cited 1× · see citing works
“It is important to note that any type of quantitative model of languages is not globally the best method and needs to be validated (Grimmer & Stewart, 2013).”
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 1× · see citing works
“…other studies where faculty suggest that the use of AI signifies the decline of critical writing skills and critical thinking (Amani et al., 2023) and that depth, assessment of credibility, and reasoning are current weaknesses of ChatGPT (Borji, 2023; Farrokhnia et al., 2023; Rudolph et al., 2023).”
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · cited 1× · see citing works
“ChatGPT and other generative models involve the collection and processing of personal data, which raises concerns about privacy and data security (Rahman & Watanobe, 2023).”
ChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853 · cited 1× · see citing works
“Sullivan et al. (2023), conducted a study focused on higher education to provide a content analysis of 100 news articles from Australia, New Zealand, the United States and the United Kingdom.”
Uniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742 · see citing works
Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning
Mhlanga, D. (2023) · doi:10.2139/ssrn.4354422 · Cited by 719 · cited 2× · see citing works
“Furthermore, Chat-GPT, which is effectively a text generating machine, does not grasp the information it generates, nor does it judge whether it is accurate or relevant, possibly leading to the creation and distribution of false and misleading information (Mhlanga, 2023).”
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., et al. (2023) · Zenodo · doi:10.5281/zenodo.7636568 · Cited by 350 · see citing works
Self-citationChatGPT: Applications, Opportunities, and Threats
Bahrini, A., Khamoshifar, M., Abbasimehr, H., Riggs, R. J., Esmaeili, M., Majdabadkohne, R. M., & Pasehvar, M. (2023) · 2023 Systems and Information Engineering Design Symposium (SIEDS) · doi:10.1109/sieds58326.2023.10137850 · Cited by 321 · see citing works
Plagiarism in the age of massive generative pre-trained transformers (GPT-3)
Dehouche, N. (2021) · Ethics in Science and Environmental Politics · doi:10.3354/esep00195 · Cited by 296 · see citing works
Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022) · arXiv · doi:10.48550/arxiv.2212.05856 · Cited by 203 · see citing works
A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?
Chen, Y., Kirshner, S. N., Ovchinnikov, A., Andiappan, M., & Jenkin, T. (2025) · Manufacturing & Service Operations Management · doi:10.1287/msom.2023.0279 · Cited by 143 · see citing works
Malik, A., Khan, M. L., & Hussain, K. (2023) · doi:10.2139/ssrn.4413516 · Cited by 59 · see citing works
Flagged first, then by citations
Research works printed but not linked: 44
- Al-Natour, S., & Turetken, O. (2020). A comparative assessment of sentiment analysis and star ratings for consumer reviews. Inter- national Journal of Information Management,54, 102132. https:// doi. org/ 10. 1016/j. ijinf omgt. 2020. 102132 · DOI
- Amani, S., White, L., Balart, T., Arora, L., Shryock, K. J., Brumbelow, K., & Watson, K. L. (2023). Generative AI perceptions: A sur- vey to measure the perceptions of faculty, staff, and students on generative AI tools in academia. arXiv. https:// doi. org/ 10. 48550/ arXiv. 2304. 14415 · DOI · Paper page
- Aydın, Ö., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations? Academic Platform Journal of Engineering and Smart Systems, 11(3), 118-134.
- Barrie, C., & Ho, J. C. T. (2021). AcademictwitteR: An R package to access the Twitter Academic Research Product Track v2 API endpoint. Journal of Open Source Software,6(62), 3272.
- Biddix, J. P., Park, H., Collom, G. D., Bailey, M. R., & Park, H. W. (2023). Discourse about higher education on Twitter in early phases of COVID-19: A crisis management social net- work analysis. Education and Information Technologies,28(8), 9957-9983.
- Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics Science & Technology Educa- tion,5(3), 235-245. https:// doi. org/ 10. 12973/ ejmste/ 75275 · DOI · Paper page
- Borji, A. (2023). A categorical archive of ChatGPT failures. arXiv. https:// doi. org/ 10. 48550/ arXiv. 2302. 03494 · DOI · Paper page
- Burch, Z. A., & Mohammed, S. (2019). Exploring faculty perceptions about classroom technology integration and acceptance: A litera- ture review. International Journal of Research in Education and Science (IJRES),5(2), 722-729.
- Cao, D., Luo, W., & Nie, G. (2023). Global DSGE models, review of economic dynamics. https:// doi. org/ 10. 1016/j. red. 2023. 01. 001 · DOI
- Dai, Y., Liu, A., & Lim, C. P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. 33rd CIRP Design Conference.
- Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Bal- akrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). So what if ChatGPT wrote it? Multidis- ciplinary perspectives on opportunities, challenges and implica- tions of generative conversational AI for research, practice and policy. International Journal of Information Management,71, 102642. https:// doi. org/ 10. 1016/j. ijinf omgt. 2023. 102642 · DOI · Paper page
- Elbagir, S., & Yang, J. (2019, March). Twitter sentiment analysis using natural language toolkit and VADER sentiment. In Proceedings of the International Multiconference of Engineers and Computer Scientists (Vol. 122, No. 16). https:// www. iaeng. org/ publi cation/ IMECS 2019/ IMECS 2019_ pp12-16. pdf
- Etter, M., Colleoni, E., Illia, L., Meggiorin, K., & D'Eugenio, A. (2018). Measuring organizational legitimacy in social media: Assessing citizens' judgments with sentiment analysis. Business & Society,57(1), 60-97. https:// doi. org/ 10. 1177/ 00076 50316 683926 · DOI
- Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching Interna- tional,1-15, 1. https:// doi. org/ 10. 1080/ 14703 297. 2023. 21958 · DOI · Paper page
- Fiesler, C., & Proferes, N. (2018). Participant perceptions of Twitter research ethics. Social Media and Society, 4(1), 1-14. https:// doi. org/ 10. 1177/ 20563 05118 763366 · DOI
- Garg, M., & Goel, A. (2023). Detection of internet cheating in online assessments using cluster analysis. In N. Sharma, A. Goje, A. Chakrabarti, & A. M. Bruckstein (Eds.), Data management, ana- lytics and innovation (pp. 85-95). Springer. https:// doi. org/ 10. 1007/ 978-981-99-1414-2_7 · DOI
- Gautreau, C. (2011). Motivational factors affecting the integration of a learning management system faculty. The Journal of Educators Online,8(1), 1-25.
- George, A. S., & George, A. S. H. (2023). A review of ChatGPT AI's impact on several business sectors. Partners Universal Interna- tional Innovation Journal,1(1), 9-23. https:// doi. org/ 10. 5281/ zenodo. 76443 59 · DOI
- Hao, K. (2023). What is ChatGPT? What to know about the Ai chatbot. The Wall Street Journal. https:// www. wsj. com/ artic les/ chatg pt-ai- chatb ot-app-expla ined-11675 865177
- Humphreys, A., & Wang, R. J. H. (2018). Automated text analysis for consumer research. Journal of Consumer Research,44(6), 1274-1306. https:// doi. org/ 10. 1093/ jcr/ ucx104 · DOI · Paper page
- Janse van Rensburg, C., Coetzee, S. A., & Schmulian, A. (2022). Developing digital creativity through authentic assessment. Assessment & Evaluation in Higher Education, 47(6), 857-877. https:// doi. org/ 10. 1080/ 02602 938. 2021. 19687 · DOI
- Jiao, W., Wang, W., Huang, J., Wang, X., & Tu, Z. (2023). Is Chat- GPT a good translator? A preliminary study. arXiv Preprint ArXiv:230108745, 1(10).
- Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management,90, 523-537. https:// doi. org/ 10. 1016/j. indma rman. 2019. 08. 003 · DOI
- Keengwe, J., & Kang, J. J. (2012). Teaching with technology: Faculty adoption of educational technology. In P. Resta (Ed.), Proceed- ings of SITE 2012-Society for Information Technology & Teacher Education International Conference (pp. 4835-4839). Association for the Advancement of Computing in Education (AACE). https:// www. learn techl ib. org/ prima ry/p/ 40372/
- King, M. R., chatGPT. (2023). A conversation on artificial intel- ligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16, 1-2. https:// doi. org/ 10. 1007/ s12195-022-00754-8 · DOI · Paper page
- Kulviwat, S., Bruner, I. I., Kumar, G. C., Nasco, A., & Clark, T. (2007). Toward a unified theory of consumer acceptance technology. Psy- chology & Marketing,24(12), 1059-1084.
- Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., & Eger, S. (2023). ChatGPT: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795. https:// doi. org/ 10. 48550/ arXiv. 2302. 13795 · DOI
- Liu, X., Burns, A. C., & Hou, Y. (2017). An investigation of brand- related user-generated content on Twitter. Journal of Advertis- ing,46(2), 236-247. https:// doi. org/ 10. 1080/ 00913 367. 2017. 12972 · DOI
- Lyu, J. C., Han, E. L., & Luli, G. K. (2021). COVID-19 vaccine-related discussion on Twitter: Topic modeling and sentiment analysis. Journal of Medical Internet Research,23(6), e24435.
- Mamo, Y. Z. (2023). Big data and innovative research methods. Inter- national Journal of Sport Communication,16(3), 352-360.
- Mamo, Y. Z., & Anagnostopoulos, C. (2023). Stakeholders'voice through social media: The case of corporate social responsibility in sport. International Journal of Sports Marketing and Sponsor- ship,24(5), 1002-1020.
- McDonald, S. M. (2012). Perception: A concept analysis. Interna- tional Journal of Nursing Knowledge,23(1), 2-9. https:// doi. org/ 10. 1111/j. 2047-3095. 2011. 01198.x · DOI
- McGee, R. W. (2023). Is ChatGPT biased against conservatives? An empirical study. SSRN Electronic Journal Https. https:// doi. org/ 10. 2139/ ssrn. 43594 05 · DOI · Paper page
- Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word- emotion association lexicon. Computational Intelligence,29(3), 436-465. https:// doi. org/ 10. 1111/j. 1467-8640. 2012. 00460.x · DOI
- O'Hallarn, B., Shapiro, S. L., Hambrick, M. E., Wittkower, D. E., Ridinger, L., & Morehead, C. A. (2018). Sport, Twitter hashtags, and the public sphere: A qualitative test of the phenomenon through a Curt Schilling case study. Journal of Sport Manage- ment, 32(4), 389-400.
- Rae, J. (2022). Connecting for creativity in higher education. Inno- vative Higher Education,48, 127-143. https:// doi. org/ 10. 1007/ s10755-022-09609-6 · DOI
- Rawlusyk, P. E. (2018). Assessment in higher education and student learning. Journal of Instructional Pedagogies, 21, 1-34.
- Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
- Saldana, J. (2015). The coding manual for qualitative researchers (3rd ed.). Sage.
- Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenge. Journal of Medical Internet Research,21(7), e13659. https:// doi. org/ 10. 2196/ 13659 · DOI · Paper page
- Shivaprasad, T. K., & Shetty, J. (2017). Sentiment analysis of product reviews: A review. 2017 International conference on inventive communication and computational technologies (ICICCT) (pp. 298-301). IEEE. https:// doi. org/ 10. 1109/ ICICCT. 2017. 79752 07 · DOI
- Strauss, A., & Corbin, J. (1995). Grounded theory methodology: An overview. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 273-285). Sage.
- Taecharungroj, V. (2023). What can ChatGPT do? Analyzing early reactions to the innovative AI chatbot on Twitter. Big Data and Cognitive Computing,7(1), 35. https:// doi. org/ 10. 3390/ bdcc7 010035 · DOI · Paper page
- Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments,10, 15. https:// doi. org/ 10. 1186/ s40561-023-00237x · DOI
Cited by
Sorted by citations
Daniel, K., Msambwa, M. M., & Wen, Z. (2025) · European Journal of Education · doi:10.1111/ejed.70036 · Cited by 71 · see citing works
Into the Unknown: AI Adoption and Job Satisfaction of Christian Faculty
Manicone, H. A., B, R., FelixOtuorimuo, I. E., Graf, L. M., Joseph, R. A., Moore, E. D., & Wilson, J. E. (2026) · Journal for the Scholarship of Teaching & Learning · doi:10.70623/ohtr6058
- Authors
- Mamo, Y., Crompton, H., Burke, D., & Nickel, C.
- Affiliations
- Old Dominion University (US)
- Corresponding author
- Mamo, Yoseph
- Venue
- TechTrends
- Published
- 2024-04-13
- Volume
- 68
- Issue
- 3
- Pages
- 520-534
- ISSN
- 1559-7075 (ISSN-L)
- 8756-3894
- OpenAlex
- W4394784173
- Cited by
- 65
- References
- 16
- Citations by year
- 2026: 29 · 2025: 29 · 2024: 7
- FWCI
- 24.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- TechTrends (publishedVersion, cc-by)
- ODU Digital Commons (Old Dominion University) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- ChatGPT, generative AI, university faculty, teacher perceptions, sentiment analysis, emotions, trust, fear, artificial intelligence in education, Twitter discourse
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review