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Research Article

Exploring public perceptions of generative AI and education: topic modelling of YouTube comments in Korea

ORCID Icon, , &
Pages 61-80 | Received 13 Aug 2023, Accepted 03 Dec 2023, Published online: 17 Dec 2023

References

  • Akkerman, S.F., & Bakker, A. (2011). Boundary crossing and boundary objects. Review of Educational Research, 81(2), 132–169. https://doi.org/10.3102/0034654311404435
  • Arun, R., Suresh, V., Veni Madhavan, C. E., Narasimha Murthy, M. N. (2010). On finding the natural number of topics with latent dirichllocation: Some observations. In M. J. Zaki, J. X. Yu, B. Ravindran & V. Pudi (Eds.), Advances in knowledge discovery and data mining (pp. 391–402). Springer.
  • Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing—16th European Symposium on Artificial Neural Networks, 1775–1781.
  • Cho, A. (2022, December 21). KakaoTalk YouTube Naver… the most frequently used apps by Koreans this year. The Korea Economic Daily. https://www.hankyung.com/article/202212210552g
  • Clark, W., Logan, K., Luckin, R., Mee, A., & Oliver, M. (2008). Beyond web 2.0: Mapping the technology landscapes of young learners. Journal of Computer Assisted Learning, 25(1), 56–69. https://doi.org/10.1111/j.1365-2729.2008.00305.x
  • Das, D., Kumar, N., Longjam, L.A., Sinha, R., Roy, A.D., Mondal, H., & Gupta, P. (2023). Assessing the capability of ChatGPT in answering first-and second-order knowledge questions on microbiology as per competency-based medical education curriculum. Cureus, 15(3). e36034. https://doi.org/10.7759/cureus.36034
  • Deveaud, R., SanJuan, E., & Bellot, P. (2014). Accurate and effective latent concept modeling for ad hoc information retrieval. Document Numérique, 17(1), 61–84. https://doi.org/10.3166/dn.17.1.61-84
  • 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., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Wirtz, J., & Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  • Eapen, T.T., Finkenstadt, D.J., Folk, J., & Venkataswamy, L. (2023). How generative AI can augment human creativity. Harvard Business Review, 101(4), 56–64.
  • Ekin, C.C., Polat, E., & Hopcan, S. (2023). Drawing the big picture of games in education: A topic modeling-based review of past 55 years. Computers & Education, 194, 104700. https://doi.org/10.1016/j.compedu.2022.104700
  • 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 International, 1–15. https://doi.org/10.1080/14703297.2023.2195846
  • Gilson, A., Safranek, C.W., Huang, T., Socrates, V., Chi, L., Taylor, R.A., & Chartash, D. (2023). How does CHATGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education, 9(1), e45312. https://doi.org/10.2196/45312
  • GMI. (2023, October 26). YouTube users statistics 2023. https://www.globalmediainsight.com/blog/youtube-users-statistics/
  • Griffiths, T.L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl.1), 5228–5235. https://doi.org/10.1073/pnas.0307752101
  • Grün, B., & Hornik, K. (2011). Topicsmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1–30. https://doi.org/10.18637/jss.v040.i13
  • Haensch, A.C., Ball, S., Herklotz, M., & Kreuter, F. (2023). Seeing ChatGPT through students’ eyes: An analysis of TikTok data. https://doi.org/10.48550/arXiv.2303.05349
  • Kasneci, E. Seßler, K. Küchemann, S. Bannert, M. Dementieva, D. Fischer, F. Grasser, U. Groh, G. Günnemann, S. Hüllermeier, E. Krusche, S. Kutyniok, G. Michaeli, T. Nerdel, C. Pfeffer, J. Poquet, O. Sailer, M. Schmidt, A. … Kasneci, G.(2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning & Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  • Kemp, S. (2023 , February 13). Digital 2023: South Korea. Datareportal. https://datareportal.com/reports/digital-2023-south-korea
  • Kim, R. (2023). Effects of ChatGPT on the cognitive processing of K-CSAT English reading tasks by Korean high school learners: A preliminary study. Secondary English Education, 16(2), 179–205. https://doi.org/10.20487/kasee.16.2.202305.179
  • Kim, Y. (2021). Do it! R text mining. EasysPublishing.
  • Kye, B.K., Park, T.J., & Cha, H.J. (2017). An exploratory study on the domestic educational utilization of ICT convergence emerging technologies and trends. Educational Information & Media Research, 23(4), 709–734. https://doi.org/10.15833/KAFEIAM.23.4.709
  • Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., & Eger, S. (2023). ChatGPT: A meta-analysis after 2.5 months. https://doi.org/10.48550/arXiv.2302.13795
  • Lim, W.M., Gunasekara, A., Pallant, J.L., Pallant, J.I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnar ̈ok or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790
  • Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
  • Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures, 12(2–3), 93–118. https://doi.org/10.1080/19312458.2018.1430754
  • McCombs, M.E., & Shaw, D.L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176–187. https://doi.org/10.1086/267990
  • Nam, S.H. (2023). Korea university issues guidelines on using ChatGPT. https://www.korea.ac.kr/user/boardList.do?boardId=366&siteId=en&page=1&id=en_060101000000&boardSeq=495142&command=albumView
  • Nguyen, H., & Jenkins, J. (2020). In or out of sync: Federal funding and research in early childhood. AERA Open, 6(4), 2332858420979568. https://doi.org/10.1177/2332858420979568
  • OpenAI. (n.d). Educator considerations for ChatGPT. https://platform.openai.com/docs/chatgpt-education
  • Park, S.M., Na, C.W., Choi, M.S., Lee, D.H., & On, B.W. (2018). KNU Korean sentiment lexicon: Bi-LSTM-based method for building a Korean sentiment lexicon. Journal of Intelligence and Information Systems, 24(4), 219–240.
  • Perloff, R.M. (2020). The dynamics of persuasion: Communication and attitudes in the twenty-first century. Routledge.
  • Polit, D.F., & Beck, C.T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147
  • Quinn, K.M., Monroe, B.L., Colaresi, M., Crespin, M.H., & Radev, D.R. (2010). How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), 209–228. https://doi.org/10.1111/j.1540-5907.2009.00427.x
  • Rahman, M.M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13116716
  • Rampersad, G., & Althiyabi, T. (2020). Fake news: Acceptance by demographics and culture on social media. Journal of Information Technology & Politics, 17(1), 1–11. https://doi.org/10.1080/19331681.2019.1686676
  • Rawas, S. (2023). ChatGPT: Empowering lifelong learning in the digital age of higher education. Education and Information Technologies, 1–14. https://doi.org/10.1007/s10639-023-12114-8
  • Rubio, D.M., Berg-Weger, M., Tebb, S., Lee, E.S., & Rauch, S. (2003). Objectifying content validity: Conducting a content validity study in social work research. Social Work Research, 27(2), 94–104. https://doi.org/10.1093/swr/27.2.94
  • Selwyn, N. (2006). Exploring the ‘digital disconnect’ between net‐savvy students and their schools. Learning, Media and Technology, 31(1), 5–17. https://doi.org/10.1080/17439880500515416
  • Selwyn, N. (2013). Distrusting educational technology: Critical questions for changing times. Routledge.
  • Star, S.L. (1989). The structure of ill-structured solutions: Boundary objects and heterogeneous distributed problem solving. In L. Gasser & M. Huhns (Eds.), Distributed artificial intelligence (pp. 37–54). Morgan Kaufmann.
  • 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/bdcc7010035
  • 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(1), 15. https://doi.org/10.1186/s40561-023-00237-x
  • Uddin, S.J., Albert, A., Ovid, A., & Alsharef, A. (2023). Leveraging ChatGPT to aid construction hazard recognition and support safety education and training. Sustainability, 15(9), 7121. https://doi.org/10.3390/su15097121
  • Wang, T., Lund, B.D., Marengo, A., Pagano, A., Mannuru, N.R., Teel, Z.A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13, 6716. https://doi.org/10.3390/app13116716
  • Wan, X., & Wang, T. (2016, August). Automatic labeling of topic models using text summaries. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany (Vol. 1, pp. 2297–2305).
  • Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies, 28(11), 1–25. https://doi.org/10.1007/s10639-023-11742-4

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