Vol. 15 No. 2 (2024): MEDIA EDUCATION – Studi, ricerche e buone pratiche
Articles

Media, media education, GAI and radical uncertainty

PDF
  • Petri Honkanen,
  • Mats Nylund
Petri Honkanen
Arcada University of Applied Sciences, Finland
Bio
Mats Nylund
Arcada University of Applied Sciences, Finland
Bio
DOI: https://doi.org/10.36253/me-16303

Published 2024-12-30

Keywords

  • generative AI,
  • media,
  • media education,
  • radical uncertainty,
  • tetrad of media effects

Copyright (c) 2024 Petri Honkanen, Mats Nylund

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The study examines the transformative potential impact of Generative AI (GAI) on society, media, and media education, focusing on the challenges and opportunities these advancements bring. GAI technologies, particularly large language models (LLMs) like GPT-4, are revolutionizing content creation, platforms, and interaction within the media landscape. This radical shift is generating both innovative educational methodologies and challenges in maintaining academic integrity and the quality of learning. The study aims to provide a comprehensive understanding of how GAI impacts media education by reshaping the content and traditional practices of media-related higher education. The research delves into three main questions: the nature of GAI as an innovation, its effect on media research and knowledge acquisition, and its implications for media education. It introduces critical concepts such as radical uncertainty, which refers to the unpredictable outcomes and impacts of GAI, making traditional forecasting and planning challenging. The paper utilizes McLuhan’s tetrad to analyze GAI’s role in media, questioning what it enhances or obsoletes, retrieves, or reverses when pushed to extremes. This theoretical approach helps in understanding the multifaceted influence of GAI on media practices and education. Overall, the research underscores the dual-edged nature of GAI in media education, where it presents significant enhancements in learning and content creation while simultaneously posing risks related to misinformation, academic integrity, and the dilution of human-centered educational practices. The study calls for a balanced approach to integrating GAI in media education, advocating for preparedness against its potential drawbacks while leveraging its capabilities to revolutionize educational paradigms.

PDF

References

  1. Ahmad, S.F., Han, H., Alam, M.M., Rehmat, M.K., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 311. https://doi.org/10.1057/s41599-023-01787-8
  2. Alawida, M., Mejri, S., Mehmood, A., Chikhaoui, B., & Abiodun, O.I., (2023). A comprehensive study of ChatGPT: Advancements, limitations, and ethical considerations in natural language processing and cybersecurity. Information, 14(8), 462. https://www.mdpi.com/2078-2489/14/8/462
  3. Allaham, M., & Diakopoulos, N. (2024). Supporting anticipatory governance using LLMs: Evaluating and aligning large language models with the news media to anticipate the negative impacts of AI. arXiv. https://arxiv.org/abs/2401.18028
  4. Alshami, A., Elsayed, M., Ali, E., Eltoukhy, A. E., & Zayed, T. (2023). Harnessing the power of ChatGPT for automating systematic review process: Methodology, case study, limitations, and future directions. Systems, 11(7), 351. https://doi.org/10.3390/systems11070351
  5. Anjos, L.S., Quincozes, S.E., Kazienko, J.F., & Quincozes, V.E. (2023). Investigating the performance of the GPT-3.5 model in fake news detection: An experimental analysis. Anais do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg), 552–557. https://www.researchgate.net/publication/378063348_Investigating_the_Performance_of_the_GPT-35_Model_in_Fake_News_Detection_An_Experimental_Analysis
  6. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN Electronic Journal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337484
  7. Bail, C. (2024). Can GenAI improve social science? Perspective Social Sciences. https://www.pnas.org/doi/10.1073/pnas.2314021121
  8. Bazelais, P., Lemay, D. J., & Doleck, T. (2024). User acceptance and adoption dynamics of ChatGPT in educational settings. Eurasia Journal of Mathematics, Science and Technology Education, 20(2), em2393. https://doi.org/10.29333/ejmste/14151
  9. Bdoor, S.Y., & Habes, M. (2024). Use ChatGPT in media content production: Digital newsrooms perspective. In Artificial intelligence in education: The power and dangers of ChatGPT in the classroom (pp. 545–561). Springer. https://link.springer.com/chapter/10.1007/978-3-031-52280-2_34
  10. Bender, E.M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 Conference on Fairness, Accountability, and Transparency (FAccT ‘21) (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922
  11. Bukar, U.A., Sayeed, M.S., Razak, S.F., Yogarayan, S., & Amodu, O.A. (2024). Text analysis of ChatGPT as a tool for academic progress or exploitation. arXiv. https://dl.acm.org/doi/abs/10.1007/s42979-024-02714-7
  12. Burger, B., Kanbach, D.K., Kraus, S., Breier, M., & Corvello, V. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), 233–241. https://doi.org/10.1108/EJIM-02-2023-0156
  13. Cheng, H., Sheng, B., Lee, A., Chaudary, V., Atanasov, A.G., Liu, N., Qiu, Y., Wong, T.Y., Tham, Y.-C., & Zheng, Y. (2024). Have AI-generated texts from LLMs infiltrated the realm of scientific writing? A large-scale analysis of preprint platforms. bioRxiv. https://doi.org/10.1101/2024.03.25.586710
  14. Chiu, T.K. (2024). Future research recommendations for transforming higher education with GenAI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197
  15. Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, 1206936. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2023.1206936/full
  16. Dougrez-Lewis, J., Akhter, M.E., He, Y., & Liakata, M. (2024). Assessing the reasoning abilities of ChatGPT in the context of claim verification. arXiv. https://arxiv.org/abs/2402.10735
  17. Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J., & Welsch, R. (2023). Gender, age, and technology education influence the adoption and appropriation of LLMs. arXiv. https://arxiv.org/abs/2310.06556
  18. Elali, F.R., & Rachid, L.N. (2023). AI-generated research paper fabrication and plagiarism in the scientific community. Patterns, 4(3), 100706. https://doi.org/10.1016/j.patter.2023.100706
  19. Espinosa, L., & Salathé, M. (2024). Use of large language models as a scalable approach to understanding public health discourse. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0000631
  20. Fan, Y., Jiang, F., Li, P., & Li, H. (2024). Uncovering the potential of ChatGPT for discourse analysis in dialogue: An empirical study. arXiv. https://arxiv.org/abs/2305.08391v2
  21. Gil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2023). A scholarly definition of artificial intelligence (AI): Advancing AI as a conceptual framework in communication research. Political Communication. https://doi.org/10.1080/10584609.2023.2290497
  22. Good, D., Maryott, K., Barlow, C. H., Jones, R., & Schwartz, R. (2024). Controlling disruptive technology: A business school’s strategic approach to ChatGPT. Developments in business simulation and experiential learning: Proceedings of the Annual ABSEL Conference (Vol. 51). https://absel-ojs-ttu.tdl.org/absel/article/view/3394
  23. Gartner. (2024). Information technology glossary: Large language models (LLMs). https://www.gartner.com/en/information-technology/glossary/large-language-models-llm
  24. Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692
  25. Grossmann, I., Feinberg, M., Parker, D.C., Christakis, N., Tetlock, P.E., & Cunningham, W.A. (2023). AI and the transformation of social science research. Science, 380(6639), 1108–1109. https://www.researchgate.net/publication/371606949_AI_and_the_transformation_of_social_science_research
  26. Guo, J., Pan, J., Guo, J., Gu, F., & Kuusisto, J. (2019). Measurement framework for assessing disruptive innovations. Technological Forecasting and Social Change, 139, 250–265. https://doi.org/10.1016/j.techfore.2018.10.015
  27. Haim, A., Salinas, A., & Nyarko, J. (2024). What’s in a name? Auditing large language models for race and gender bias. arXiv. https://arxiv.org/abs/2402.14875v2
  28. Hamilton, L., Elliott, D., Quick, A., Smith, S., & Choplin, V. (2023). Exploring the use of AI in qualitative analysis: A comparative study of guaranteed income data. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231201504
  29. Heumann, M., Kraschewski, T., & Breitner, M.H. (2023). ChatGPT and GPTZero in research and social media: A sentiment-and topic-based analysis. AMCIS 2023 Proceedings, 6. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4467646
  30. Holmström, J., & Carroll, N. (2024). How organizations can innovate with GenAI. Business Horizons. https://doi.org/10.1016/j.bushor.2024.02.010
  31. Hubert, K.F., Awa, K.N., & Zabelina, D.L. (2024). The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks. Scientific Reports, 14, 3440. https://doi.org/10.1038/s41598-024-53303-w
  32. Huh, J., Nelson, M.R., & Russell, C.A. (2023). ChatGPT, AI advertising, and advertising research and education. Journal of Advertising, 52(4), 477–482. https://doi.org/10.1080/00913367.2023.2227013
  33. Karanouh, M. (2023). Mapping ChatGPT in mainstream media: Early quantitative insights through sentiment analysis and word frequency analysis. arXiv. https://arxiv.org/abs/2305.18340
  34. Khlaif, Z.N., Mousa, A., Hattab, M.K., Itmazi, J., Hassan, A.A., Sanmugam, M., & Ayyoub, A. (2023). The potential and concerns of using AI in scientific research: ChatGPT performance evaluation. JMIR Medical Education, 9(1), e47049. https://doi.org/10.2196/47049
  35. Korkmaz, A., Aktürk, C., & Talan, T. (2023). Analyzing the user’s sentiments of ChatGPT using Twitter data. Iraqi Journal for Computer Science and Mathematics, 4(2), 202–214. https://journal.esj.edu.iq/index.php/IJCM/article/view/618
  36. Koubaa, A., Boulila, W., Ghouti, L., Alzahem, A., & Latif, S. (2023). Exploring ChatGPT capabilities and limitations: A survey. IEEE Access, 11, 118698–118. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10290719
  37. Li, Z. (2024). The impact of artificial intelligence technology innovation on economic development: From the perspective of GenAI products. Journal of Education, Humanities and Social Sciences, 27, 565–574. https://doi.org/10.54097/8eb1ks76
  38. Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., & Hemphill, L. (2023). ChatGPT in education: A discourse analysis of worries and concerns on social media. Education and Information Technologies, 29(1), 10729–10762. https://doi.org/10.1007/s10639-023-12256-9
  39. Lian, Y., Tang, H., Xiang, M., & Dong, X. (2024). Public attitudes and sentiments toward ChatGPT in China: A text mining analysis based on social media. Technology in Society, 102442. https://doi.org/10.1016/j.techsoc.2023.102442
  40. Liu, J., Liu, C., Lv, R., Zhou, K., & Zhang, Y. (2023). Is ChatGPT a good recommender? A preliminary study. CIKM 2023 GenRec Workshop. https://arxiv.org/abs/2304.10149
  41. Luttrell, R., Wallace, A., McCollough, C., & Lee, J. (2020). The Digital Divide: Addressing artificial intelligence in communication education. Journalism & Mass Communication Educator, 75(4), 470–482. https://journals.sagepub.com/doi/full/10.1177/1077695820925286
  42. Lynch, C.J., Jensen, E.J., Zamponi, V., O’Brien, K., Frydenlund, E., & Gore, R. (2023). A structured narrative prompt for prompting narratives from large language models: Sentiment assessment of ChatGPT-generated narratives and real tweets. Future Internet, 15(12), 375. https://www.mdpi.com/1999-5903/15/12/375
  43. McLuhan, M., & McLuhan, E. (1988). The laws of media: The new science. University of Toronto Press.
  44. Menon, D., & Shilpa, K. (2023). Chatting with ChatGPT: Analyzing the factors influencing users’ intention to use the OpenAI’s ChatGPT using the UTAUT model. Heliyon, 9(11), e20962. https://doi.org/10.1016/j.heliyon.2023.e20962
  45. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693?locale=en
  46. Olinski, M., Krukowski, K., & Siecinski, K. (2024). Bibliometric overview of ChatGPT: New perspectives in social sciences. Publications, 12(1), 9. https://doi.org/10.3390/publications12010009
  47. Pavlik, J.V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator. https://doi.org/10.1177/10776958221149577
  48. Pock, M., Ye, A., & Moore, J. (2023). LLMs grasp morality in concept. NeurIPS 2023 Moral Psychology and Moral Philosophy Workshop. https://arxiv.org/abs/2311.02294
  49. Polat, H., Topuz, A.C., Yildiz, M., Taslibeyaz, E., & Kursun, E. (2024). A bibliometric analysis of research on ChatGPT in education. International Journal of Technology in Education, 7(1), 59–85. https://doi.org/10.46328/ijte.606
  50. Polyportis, A., & Pahos, N. (2023). Navigating the perils of artificial intelligence: A focused review on ChatGPT and responsible research and innovation. Humanities and Social Sciences Communications, 11(1), 107. https://doi.org/10.1057/s41599-023-02464-6
  51. Rathje, S., Mirea, D., Sucholutsky, I., Marjieh, R., Robertson, C.E., & Van Bavel, J.J. (2024). GPT is an effective tool for multilingual psychological text analysis. PNAS. https://doi.org/10.1073/pnas.2308950121
  52. Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects. Business Horizons. https://doi.org/10.1016/j.bushor.2024.04.009
  53. Romano, W., Sharif, O., Basak, M., Gatto, J., & Preum, S.M. (2024). Theme-driven keyphrase extraction to analyze social media discourse. Proceedings of the Eighteenth International AAAI Conference on Web and Social Media (ICWSM2024). https://ojs.aaai.org/index.php/ICWSM/article/view/31391/33551
  54. Rozado, D. (2023). The political biases of ChatGPT. Social Sciences, 12(3), 148. https://doi.org/10.3390/socsci12030148
  55. Shou, S. (2023). Implement GenAI tools in analytics. Master of Science in Management and Systems at the Division of Programs, Business School of Professional Studies, New York University. https://archive.nyu.edu/handle/2451/69533
  56. Talafidaryani, M., & Moro, S. (2024). Public perception of ChatGPT on Reddit social media platform: Topic modeling and sentiment analysis study. SSRN. http://dx.doi.org/10.2139/ssrn.4716839
  57. Tang, Y. (2023). Understanding societal values of ChatGPT. McKelvey School of Engineering Theses & Dissertations (848). https://openscholarship.wustl.edu/eng_etds/848
  58. Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation, and datafication in education. Learning, Media and Technology, 48(1), 1–5. https://doi.org/10.1080/17439884.2023.2167830
  59. de Winter, J. (2024). Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among 2222 scientific abstracts. Scientometrics, 129(3), 2469–2487. https://doi.org/10.1007/s11192-024-04939-y
  60. Zhang, H., Wu, C., Xie, J., Lyu, Y., Cai, J., & Carroll, J.M. (2023a). Redefining qualitative analysis in the AI era: Utilizing ChatGPT for efficient thematic analysis. arXiv. https://arxiv.org/abs/2309.10771
  61. Zhang, H., Wu, C., Xie, J., Kim, C., & Carroll, J.M. (2023b). QualiGPT: GPT as an easy-to-use tool for qualitative coding. arXiv. https://arxiv.org/abs/2310.07061
  62. Zhou, Y., Xu, P., Wang, X., Lu, X., Gao, G., & Ai, W. (2024). Emojis decoded: Leveraging ChatGPT for enhanced understanding in social media communications. arXiv. https://arxiv.org/abs/2402.01681
  63. Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024). Can large language models transform computational social science? Computational Linguistics, 50(1), 237–291. https://doi.org/10.1162/coli_a_00502