Public sentiment and ethical considerations of ChatGPT in higher education: Insights from data analytics of conversations on platform X

Ghanem Ayed Elhersh, Haneen Khaled Alqawasmeh

Article ID: 7518
Vol 8, Issue 12, 2024

VIEWS - 192 (Abstract) 90 (PDF)

Abstract


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.


Keywords


generative AI; higher education; text mining; academic integrity; public engagement

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References


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

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

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

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

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

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

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

Cohen, L., Manion, L., & Morrison, K. (2002). Research Methods in Education. Routledge. https://doi.org/10.4324/9780203224342

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

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

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Sage Publications.

Crofts, K., & Bisman, J. (2010). Interrogating accountability. Qualitative Research in Accounting & Management, 7(2), 180–207. https://doi.org/10.1108/11766091011050859

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

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

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

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

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

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).

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

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

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

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

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

Leximancer. (2022). Leximancer: From words to meaning to insight. Available online: https://www.leximancer.com/ (accessed on 2 May 2024).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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




DOI: https://doi.org/10.24294/jipd.v8i12.7518

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