In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.

1. Ahmad, B., Sun, J., You, Q., et al. (2022). Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks. Biomedicines, 10(2), 223. https://doi.org/10.3390/biomedicines10020223
2. Amirian, J., van Toll, W., Hayet, J.-B., et al. (2019). Data-Driven Crowd Simulation with Generative Adversarial Networks. In: Proceedings of the 32nd International Conference on Computer Animation and Social Agents. https://doi.org/10.1145/3328756.3328769
3. Anantrasirichai, N., & Bull, D. (2021). Artificial intelligence in the creative industries: a review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7
4. Bahroun, Z., Anane, C., Ahmed, V., et al. (2023). Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis. Sustainability, 15(17), 12983. https://doi.org/10.3390/su151712983
5. Boscardin, C. K., Gin, B., Golde, P. B., et al. (2023). ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Academic Medicine, 99(1), 22–27. https://doi.org/10.1097/acm.0000000000005439
6. Cai, M., Luo, H., Meng, X., et al. (2023). Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management, 60(2), 103197. https://doi.org/10.1016/j.ipm.2022.103197
7. Cetinic, E., & She, J. (2022). Understanding and Creating Art with AI: Review and Outlook. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(2), 1–22. https://doi.org/10.1145/3475799
8. Cohen, L., Manion, L., & Morrison, K. (2002). Research Methods in Education. Routledge. https://doi.org/10.4324/9780203224342
9. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
10. Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3). https://doi.org/10.53761/1.20.3.02
11. Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Sage Publications.
12. Crofts, K., & Bisman, J. (2010). Interrogating accountability. Qualitative Research in Accounting & Management, 7(2), 180–207. https://doi.org/10.1108/11766091011050859
13. Dianova, V. G., & Schultz, M. D. (2023). Discussing ChatGPT’s implications for industry and higher education: The case for transdisciplinarity and digital humanities. Industry and Higher Education, 37(5), 593–600. https://doi.org/10.1177/09504222231199989
14. Diwan, C., Srinivasa, S., Suri, G., et al. (2023). AI-based learning content generation and learning pathway augmentation to increase learner engagement. Computers and Education: Artificial Intelligence, 4, 100110. https://doi.org/10.1016/j.caeai.2022.100110
15. Elbanna, S., & Armstrong, L. (2023). Exploring the integration of ChatGPT in education: adapting for the future. Management & Sustainability: An Arab Review, 3(1), 16–29. https://doi.org/10.1108/msar-03-2023-0016
16. Eysenbach, G. (2023). The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation with ChatGPT and a Call for Papers. JMIR Medical Education, 9. https://doi.org/10.2196/46885
17. Feuerriegel, S., Hartmann, J., Janiesch, C., et al. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7
18. Garfinkle, A. (2023). ChatGPT on track to surpass 100 million users faster than TikTok or Instagram: UBS. Available online: https://finance.yahoo.com/news/chatgpt-on-track-to-surpass-100-million-users-faster-than-tiktok-or-instagram-ubs-214423357.html (accessed on 2 May 2024).
19. Ghosh, A., & Bir, A. (2023). Evaluating ChatGPT’s Ability to Solve Higher-Order Questions on the Competency-Based Medical Education Curriculum in Medical Biochemistry. Cureus. https://doi.org/10.7759/cureus.37023
20. Hardian, R. W., Prasetyo, P. E., Khaira, U., et al. (2021). Sentiment Analysis of Online Lectures on Social Media Twitter During the Covid-19 Pandemic Using Sentistrength Algorithm (Indonesian). MALCOM: Indonesian Journal of Machine Learning and Computer Science, 1(2), 138–143. https://doi.org/10.57152/malcom.v1i2.15
21. Hughes, R. T., Zhu, L., & Bednarz, T. (2021). Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends. Frontiers in Artificial Intelligence, 4. https://doi.org/10.3389/frai.2021.604234
22. Hussain, K., Khan, M. L., & Malik, A. (2024). Exploring audience engagement with ChatGPT-related content on YouTube: Implications for content creators and AI tool developers. Digital Business, 4(1), 100071. https://doi.org/10.1016/j.digbus.2023.100071
23. Lee, M., Liang, P., & Yang, Q. (2022). CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities. CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3502030
24. Leximancer. (2022). Leximancer: From words to meaning to insight. Available online: https://www.leximancer.com/ (accessed on 2 May 2024).
25. Li, L., Ma, Z., Fan, L., et al. (2023). ChatGPT in education: a discourse analysis of worries and concerns on social media. Education and Information Technologies, 29(9), 10729–10762. https://doi.org/10.1007/s10639-023-12256-9
26. Lund, B. D., Wang, T., Mannuru, N. R., et al. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581. Portico. https://doi.org/10.1002/asi.24750
27. Miller, A. (2018). Text Mining Digital Humanities Projects: Assessing Content Analysis Capabilities of Voyant Tools. Journal of Web Librarianship, 12(3), 169–197. https://doi.org/10.1080/19322909.2018.1479673
28. Nguyen, D. (2023). How news media frame data risks in their coverage of big data and AI. Internet Policy Review, 12(2). https://doi.org/10.14763/2023.2.1708
29. Öztürk, N., & Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136–147. https://doi.org/10.1016/j.tele.2017.10.006
30. Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 78(1), 84–93. https://doi.org/10.1177/10776958221149577
31. Peres, R., Schreier, M., Schweidel, D., et al. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275. https://doi.org/10.1016/j.ijresmar.2023.03.001
32. Rudolph, J., Tan, S., Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Ed-tech Reviews, 6(1). https://doi.org/10.37074/jalt.2023.6.1.9
33. Sharifzadeh, N., Kharrazi, H., Nazari, E., et al. (2020). Health Education Serious Games Targeting Health Care Providers, Patients, and Public Health Users: Scoping Review. JMIR Serious Games, 8(1), e13459. https://doi.org/10.2196/13459
34. Sian Lee, C., & Hoe‐Lian Goh, D. (2013). “Gone too soon”: did Twitter grieve for Michael Jackson? Online Information Review, 37(3), 462–478. https://doi.org/10.1108/oir-05-2012-0082
35. Singh, J., Pandey, D., & Singh, A. K. (2023). Event detection from real-time twitter streaming data using community detection algorithm. Multimedia Tools and Applications, 83(8), 23437–23464. https://doi.org/10.1007/s11042-023-16263-3
36. Sop, S. A., & Kurçer, D. (2024). What if ChatGPT generates quantitative research data? A case study in tourism. Journal of Hospitality and Tourism Technology, 15(2), 329–343. https://doi.org/10.1108/jhtt-08-2023-0237
37. Sotiriadou, P., Brouwers, J., & Le, T.-A. (2014). Choosing a qualitative data analysis tool: a comparison of NVivo and Leximancer. Annals of Leisure Research, 17(2), 218–234. https://doi.org/10.1080/11745398.2014.902292
38. Sung, E. (Christine), Bae, S., Han, D.-I. D., & Kwon, O. (2021). Consumer engagement via interactive artificial intelligence and mixed reality. International Journal of Information Management, 60, 102382. https://doi.org/10.1016/j.ijinfomgt.2021.102382
39. Thelwall, M. (2022). Sentiment Analysis. In: The SAGE Handbook of Social Media Research Methods. SAGE Publications Ltd. pp. 521–530. https://doi.org/10.4135/9781529782943.n37
40. Tlili, A., Shehata, B., Adarkwah, M. A., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00237-x
41. van de Ridder, J. M. M., Shoja, M. M., & Rajput, V. (2023). Finding the Place of ChatGPT in Medical Education. Academic Medicine, 98(8), 867–867. https://doi.org/10.1097/acm.0000000000005254
42. Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799–813. https://doi.org/10.1177/0165551515598926
43. Wang, Z., Zhang, S., Zhao, Y., et al. (2023). Risk prediction and credibility detection of network public opinion using blockchain technology. Technological Forecasting and Social Change, 187, 122177. https://doi.org/10.1016/j.techfore.2022.122177
44. Wu, R., & Yu, Z. (2023). Do AI chatbots improve students learning outcomes? Evidence from a meta‐analysis. British Journal of Educational Technology, 55(1), 10–33. Portico. https://doi.org/10.1111/bjet.13334
45. Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9(1). https://doi.org/10.1186/s40594-022-00377-5
46. Yoo, E., Rand, W., Eftekhar, M., et al. (2016). Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. Journal of Operations Management, 45(1), 123–133. Portico. https://doi.org/10.1016/j.jom.2016.05.007
47. Zirar, A. (2023). Exploring the impact of language models, such as ChatGPT, on student learning and assessment. Review of Education, 11(3). Portico. https://doi.org/10.1002/rev3.3433