Journal Evaluation in Education (JEE)
Journal Evaluation in Education (JEE)

an Open Access Journal

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Journal Evaluation in Education (JEE)

an Open Access Journal


Education in the ChatGPT Era: A Sentiment Analysis of Public Discourse on the Role of Language Models in Education

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  • Purpose of the study: This research explored the public discourse on the role of language models in education, particularly ChatGPT.

    Methodology: The study employed sentiment analysis, word cloud analysis, and thematic analysis of YouTube video news transcripts.

    Main Findings: The study identified key themes and public perceptions surrounding AI's role in education. The analysis revealed frequent mentions of AI, education, and learning, highlighting AI's transformative potential in personalizing education and improving administrative efficiency. The study also emphasizes significant concerns about academic integrity, with terms like cheating and plagiarism reflecting ethical apprehensions. The sentiment analysis revealed a compound sentiment of 0.866, a generally positive perception of AI's impact on education. However, some negative sentiments were recorded, particularly around data privacy and potential biases. The thematic analysis identified five key topics: the future of AI and human intelligence, AI in education and learning enhancement, innovative teaching tools and technologies, AI-assisted writing and learning tools, and ethics and academic integrity in AI usage. The findings revealed the importance of developing robust ethical guidelines and policies for integrating AI into educational settings and the need for further research into the long-term impacts of AI on teaching methodologies.

    Novelty/Originality of this study: The research used YouTube news transcripts to explore public discourse on ChatGPT's role in education, providing real-time insights into societal perceptions. Combining sentiment, word cloud, and thematic analyses offers a balanced examination of the transformative potential and ethical concerns surrounding AI in education.

  • How to cite

    [1]
    “Education in the ChatGPT Era: A Sentiment Analysis of Public Discourse on the Role of Language Models in Education”, Jor. Eva. Edu, vol. 5, no. 4, pp. 144–154, Oct. 2024, doi: 10.37251/jee.v5i4.1151.
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    1. M. Philogene, S. Zhiyuan, and P. Nyoni, “Teacher professionalism development in TVET system: Preparedness, In-Service trainings and challenges”, Journal Evaluation in Education (JEE), vol. 5, no. 3, pp. 107-117, 2024, doi: 10.37251/jee.v5i3.967. DOI: https://doi.org/10.37251/jee.v5i3.967
    2. D. Baidoo-Anu, and L. O. Ansah, “Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning”, Journal of AI, vol. 7, no. 1, pp. 52-62, 2023, doi: 10.61969/jai.1337500. DOI: https://doi.org/10.61969/jai.1337500
    3. A. Johri, A. S. Katz, J. Qadir, and A. Hingle, “Generative artificial intelligence and engineering education”, Journal of Engineering Education, vol. 112, no. 3, pp. 572-577, 2023, doi: 10.1002/jee.20537. DOI: https://doi.org/10.1002/jee.20537
    4. M. Jovanović, and M. Campbell, “Generative Artificial Intelligence: Trends and Prospects”, 2022, doi: 0.1109/MC.2022.3192720.
    5. L. Hu, “Generative AI and Future<”, 2023, Retrieved on January 23 from https://pub.towardsai.net/generativeai-and-future-c3b1695876f2.
    6. OpenAI. Available online: https://openai.com (accessed on 12 March 2024).
    7. C. K. Lo, “What is the impact of ChatGPT on education? A rapid review of the literature”, Education Sciences, vol. 13, no. 4, pp. 410, 2023, doi: 10.3390/educsci13040410. DOI: https://doi.org/10.3390/educsci13040410
    8. J. Crawford, M. Cowling, and K. Allen, “Leadership is needed for ethical ChatGPT: Character, assessment, and learning using Artificial Intelligence (AI),” Journal of University Teaching & Learning Practice, vol. 20, no. 3, pp. 1-21, 2022, doi: 10.53761/1.20.3.02. DOI: https://doi.org/10.53761/1.20.3.02
    9. J. Qadir, “Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education”, TechRxiv Prepr. 2022, doi: 10.36227/techrxiv.21789434. DOI: https://doi.org/10.36227/techrxiv.21789434.v1
    10. M. M. Rahman, and Y. Watanobe, “ChatGPT for education and research: Opportunities, threats, and strategies”, Applied Sciences, vol. 13, no. 9, pp. 5783, 2023, doi: 10.3390/app13095783. DOI: https://doi.org/10.3390/app13095783
    11. S. Shahriar, and K. Hayawi, “Let’s have a chat! A conversation with ChatGPT: Technology, applications, and limitations. arXiv preprint”, arXiv:2302.13817, doi: 10.48550/arXiv.2302.13817.
    12. P. Limna, T. Kraiwanit, K. Jangjarat, P. Klayklung, and P. Chocksathaporn, “The use of ChatGPT in the digital era: Perspectives on chatbot implementation”, Journal of Applied Learning and Teaching, vol. 6, no. 1, 2023, doi: 10.37074/jalt.2023.6.1.32 DOI: https://doi.org/10.37074/jalt.2023.6.1.32
    13. J. A. Lossio-Ventura, R. Weger, A. Y. Lee, E. P. Guinee, J. Chung, L. Atlas,... and F. Pereira, “A comparison of chatgpt and fine-tuned open pre-trained transformers (opt) against widely used sentiment analysis tools: Sentiment analysis of covid-19 survey data”, JMIR Mental Health, 11, e50150, 2024, doi: 10.2196/50150 DOI: https://doi.org/10.2196/50150
    14. J. W. Creswell, “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches”, Sage Publications, USA, 2023.
    15. L. J. B. Caluza, “Deciphering published articles on cyberterrorism: A latent Dirichlet allocation algorithm application”, International Journal of Data Mining, Modelling and Management, vol. 11, no. 1, pp. 87-101, 2019. DOI: https://doi.org/10.1504/IJDMMM.2019.10016836
    16. S. Vijayarani, M. J. Ilamathi, and M. Nithya, “Preprocessing techniques for text mining-an overview,” International Journal of Computer Science & Communication Networks, vol. 5, no. 1, pp. 7-16, 2015 DOI: https://doi.org/10.5121/ijcga.2015.5105
    17. C. Ramasubramanian, and R. Ramya, “Effective pre-processing activities in text mining using improved porter‟s stemming algorithm”, International Journal of Advanced Research in Computer and Communication Engineering,vol. 2, no. 12, 2013.
    18. A. Alfajri, D. Richasdy, and M. A. Bijaksana, “Topic modelling using Non-Negative Matrix Factorization (NMF) for telkom university entry selection from instagram comments”, Journal of Computer System and Informatics (JoSYC), vol. 3, no. 4, pp. 485-492, 2022, doi: 10.47065/josyc.v3i4.2212. DOI: https://doi.org/10.47065/josyc.v3i4.2212
    19. R. K. Mishra, S. Urolagin, J. A. Jothi, A. S. Neogi, and N. Nawaz, “Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic”, Front. Comput. Sci. 3:775368, 2022, doi: 10.3389/fcomp.2021.775368. DOI: https://doi.org/10.3389/fcomp.2021.775368
    20. V. Bonta, N. Kumaresh, and N. Janardhan, “A comprehensive study on lexicon based approaches for sentiment analysis”, Ajcst, vol. 8, n.o. (S2), 1–6, 2019, doi:10.51983/AJCST-2019.8.S2.2037 DOI: https://doi.org/10.51983/ajcst-2019.8.S2.2037
    21. N. Swarnkar, “VADER Sentiment Analysis: A Complete Guide, Algo Trading and More”, (2020, March 21). Retrieved from Quantinsti: https://blog.quantinsti.com/vader-sentiment/
    22. A. Farkhod, A. Abdusalomov, F. Makhmudov, and Y. I. Cho, “LDA-based topic modeling sentiment analysis using topic/document/sentence (TDS) model”, Applied Sciences, vol. 11, no. 23, pp. 11091, 2021, doi: 10.3390/app112311091. DOI: https://doi.org/10.3390/app112311091
    23. A. Agarwal, B. Xie, I. Vovsha, O, Rambow, R. J. Passonneau, “Sentiment analysis of twitter data”, In Proceedings of the Workshop on Language in Social Media (LSM 2011), Portland, Oregon, 23 June 2011.
    24. V. Eidelman, J. Boyd-Graber, P. Resnik, “Topic models for dynamic translation model adaption”, In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Jeju Island, Korea, 8–14 July 2012.
    25. P. van Kessel, “Interpreting and validating topic models”, (2019, August 2). Retrieved from Medium: https://medium.com/pew-research-center-decoded/interpreting-and-validating-topic-models-ff8f67e07a32
    26. J. Demšar, T. Curk, A. Erjavec, C. Gorup, T. Hočevar, M. Milutinovič,... and B. Zupan, “Orange: data mining toolbox in Python”, the Journal of machine Learning research, vol. 14, no. 1, pp. 2349-2353, 2013.
    27. B. U. Zaman, “Transforming education through ai, benefits, risks, and ethical considerations”, Authorea Preprints, 2023, doi: 10.36227/techrxiv.24231583.v1 DOI: https://doi.org/10.36227/techrxiv.24231583.v1
    28. J. Young, “The rise of artificial intelligence in education”, International Journal of Innovative Research and Development, 2024, doi: https://doi.org/10.24940/ijird/2024/v13/i2/FEB24019 DOI: https://doi.org/10.24940/ijird/2024/v13/i2/FEB24019
    29. D. R. Cotton, P. A. Cotton, and J. R. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT”, Innovations in education and teaching international, vol. 61, no. 2, pp. 228-239, 2024, doi: 10.1080/14703297.2023.2190148 DOI: https://doi.org/10.1080/14703297.2023.2190148
    30. J. Miao, C. Thongprayoon, S. Suppadungsuk, O. A. Garcia Valencia, F. Qureshi, and W. Cheungpasitporn, “Ethical dilemmas in using AI for academic writing and an example framework for peer review in nephrology academia: A narrative review”, Clinics and Practice, vol. 14, no. 1, pp. 89-105, 2023, doi: 10.3390/clinpract14010008 DOI: https://doi.org/10.3390/clinpract14010008
    31. M. Montenegro-Rueda, J. Fernández-Cerero, J. M. Fernández-Batanero, and E. López-Meneses, “Impact of the implementation of ChatGPT in education: A systematic review”, Computers, vol. 12, no. 8, pp. 153, 2023, doi: 10.3390/computers12080153 DOI: https://doi.org/10.3390/computers12080153
    32. M. H. Jarrahi, C. Lutz, and G. Newlands, “Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation”, Big Data & Society, vol. 9, no. 2, 2022, doi: https://doi.org/10.1177/2053951722114282 DOI: https://doi.org/10.1177/20539517221142824
    33. E. Mercier-Laurent, “The Future of Artificial Intelligence: Empowering Humanity and Help Protecting Planet”. In Computer Sciences & Mathematics Forum (Vol. 8, No. 1, p. 38), (2023, August). MDPI, doi: 10.3390/cmsf2023008038 DOI: https://doi.org/10.3390/cmsf2023008038
    34. B. Zohuri, and F. M. Rahmani, F. M. “Artificial intelligence versus human intelligence: A new technological race”, Acta Scientific Pharmaceutical Sciences, vol. 4, no. 5, 2020, doi: 10.31080/ASPS.2020.04.0530 DOI: https://doi.org/10.31080/ASPS.2020.04.0530
    35. A. de Oliveira Silva, and D. dos Santos Janes, “The emergence of ChatGPT and its implications for education and academic research in the 21st century”, Review of Artificial Intelligence in Education, vol. 2, pp. e06-e06, 2021, doi: 10.37497/rev.artif.intell.education.v2i00.6 DOI: https://doi.org/10.37497/rev.artif.intell.education.v2i00.6
    36. J. J. B. Aguiar, “ChatGPT as an educational support tool: an analysis of its potential in the teaching and learning process”, Caderno Pedagógico, vol. 21, no. 2, pp. e2660-e2660, 2024, doi: 10.54033/cadpedv21n2-019. DOI: https://doi.org/10.54033/cadpedv21n2-019
    37. N. C. A. Ashwini, N. M. N. Kumar, and S. V, “Leveraging artificial intelligence in education: Transforming the learning landscape”, International Research Journal of Computer Science, 2023, doi: 10.26562/irjcs.2023.v1005.16 DOI: https://doi.org/10.26562/irjcs.2023.v1005.16
    38. D. Z. Obidovna, “The pedagogical-psychological aspects of artificial intelligence technologies in integrative education”, International Journal Of Literature And Languages, vol. 4, no. 3, pp. 13-19, 2024, doi: 10.37547/ijll/Volume04Issue03-03 DOI: https://doi.org/10.37547/ijll/Volume04Issue03-03
    39. O. O. Adeleye, C. A. Eden, and I. S. Adeniyi, “Innovative teaching methodologies in the era of artificial intelligence: A review of inclusive educational practices”, World Journal of Advanced Engineering Technology and Sciences, vol. 11, no. 2, pp. 069-079, 2024, doi: 10.30574/wjaets.2024.11.2.0091. DOI: https://doi.org/10.30574/wjaets.2024.11.2.0091
    40. C. Song, and Y. Song, “Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students”, Frontiers in Psychology, vol. 14, pp. 1260843, 2023, doi: 10.3389/fpsyg.2023.1260843 DOI: https://doi.org/10.3389/fpsyg.2023.1260843
    41. L. Wu, “AI-based writing tools: Empowering students to achieve writing success”, Advances in Educational Technology and Psychology, vol. 8, no. 2, pp. 40-44, 2024, doi: 10.23977/aetp.2024.080206 DOI: https://doi.org/10.23977/aetp.2024.080206
    42. M. J. K. O. Jian, “Personalized learning through AI”, Advances in Engineering Innovation, vol. 5, no. 1, pp. 2023, doi: 10.54254/2977-3903/5/2023039 DOI: https://doi.org/10.54254/2977-3903/5/2023039
    43. I. Celik, M. Dindar, H. Muukkonen, and S. Järvelä, “The promises and challenges of artificial intelligence for teachers: A systematic review of research”, TechTrends, vol. 66, no. 4, pp. 616-630, 2022, doi: 10.1007/s11528-022-00715-y. DOI: https://doi.org/10.1007/s11528-022-00715-y
    44. C. Westfall, “Educators Battle Plagiarism As 89% Of Students Admit To Using OpenAI’s ChatGPT For Homework”, C. (2023, Jan 28).
    45. P. P. Santra, and D. Majhi, “Scholarly communication and machine-generated text: is it finally ai vs ai in plagiarism detection?. Journal of Information and Knowledge, 175-183, 2023, doi: 10.17821/srels/2023/v60i3/171028. DOI: https://doi.org/10.17821/srels/2023/v60i3/171028
    46. J. Sreerama, and G. Krishnamoorthy, “Ethical Considerations in AI Addressing Bias and Fairness in Machine Learning Models”, Journal of Knowledge Learning and Science Technology, vol. 1, no. 1, pp. 130-138, doi: 10.60087/jklst.vol1.n1.p138. DOI: https://doi.org/10.60087/jklst.vol1.n1.p138