journal article·2025-04-02Open access

Transforming Education in the AI Era: A Technology–Organization–Environment Framework Inquiry into Public Discourse

Jinqiao Zhou, Hongfeng Zhang

Applied Sciences· Volume 15· Issue 7

Abstract

The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion topic mining and SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms in China. Through a detailed analysis of the data, this study examines the distribution of positive and negative emotions and identifies six topics. The study further utilizes visual tools such as word clouds and heatmaps to present the research findings. The results indicate that the emotional polarity across all topics is characterized by a predominance of positive emotions over negative ones. Moreover, an analysis of the keywords across the six topics reveals that each has its own emphasis, yet there are overlaps between them. Therefore, this study, through quantitative methods, also reflects the complex interconnections among the elements within the educational ecosystem. Additionally, this study integrates the six identified topics with the Technology–Organization–Environment (TOE) framework to explore the broad impact of AIGC on education from the perspectives of technology, organization, and environment. This research provides a novel perspective on the emotional attitudes and key concerns of the Chinese public regarding the use of AIGC in education.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Ethics and Social Impacts of AI · AI in Service Interactions · Smart Cities and Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Humanities and Social Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China

Funding

Macao Polytechnic University

References (85)

Show source-provided references
1. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration
2. Exploring the limits of transfer learning with a unified text-to-text transformer
3. Du, H., Li, Z., Niyato, D., Kang, J., Xiong, Z., and Kim, D.I. (2023). Enabling AI-generated content (AIGC) services in wireless edge networks. arXiv.10.1109/MWC.004.2300015 ↗
4. Visuals to text: A comprehensive review on automatic image captioning10.1109/JAS.2022.105734 ↗
5. Exploring public response to ChatGPT with sentiment analysis and knowledge mapping10.1109/ACCESS.2024.3386362 ↗
6. Public attitudes and sentiments toward ChatGPT in China: A text mining analysis based on social media10.1016/j.techsoc.2023.102442 ↗
7. Xu, C., Guo, J., Zeng, J., Meng, S., Chu, X., Cao, J., and Wang, T. (2024, January 23–26). Enhancing AI-Generated Content Efficiency Through Adaptive Multi-Edge Collaboration. Proceedings of the 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), Jersey City, NJ, USA.10.1109/ICDCS60910.2024.00093 ↗
8. AIGC Related Context: A New Communication Culture For Human
9. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy10.1016/j.ijinfomgt.2023.102642 ↗
10. The impact of Smart city construction on labour spatial allocation: Evidence from China10.1080/00036846.2023.2186367 ↗
11. Examining science education in ChatGPT: An exploratory study of generative artificial intelligence10.1007/s10956-023-10039-y ↗
12. AIGC challenges and opportunities related to public safety: A case study of ChatGPT
13. Seldon, A., and Abidoye, O. (2018). The Fourth Education Revolution, Legend Press Ltd.
14. Explicating AI literacy of employees at digital workplaces10.1109/TEM.2021.3138503 ↗
15. Leveraging artificial intelligence in business: Implications, applications and methods10.1080/09537325.2021.1883583 ↗
16. Exploring the impact of artificial intelligence on teaching and learning in higher education10.1186/s41039-017-0062-8 ↗
17. The promises and challenges of artificial intelligence for teachers: A systematic review of research10.1007/s11528-022-00715-y ↗
18. Application and theory gaps during the rise of artificial intelligence in education10.1016/j.caeai.2020.100002 ↗
19. Artificial intelligence applications in K-12 education: A systematic literature review10.1109/ACCESS.2022.3179356 ↗
20. Preparing teachers for the application of AI-powered technologies in foreign language education10.2478/jolace-2019-0025 ↗
21. Yu, L., Zhou, H., Shao, J. (2024). The Theory and Practice of Home-School-Community Collaborative Education in the Era of Artificial Intelligence. Envisioning the Future of Education Through Design, Springer.10.1007/978-981-97-0076-9_15 ↗
22. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research10.1016/j.sapharm.2023.05.016 ↗
23. AI-Driven transformations in higher education: Opportunities and challenges
24. Empowering education development through AIGC: A systematic literature review10.1007/s10639-024-12549-7 ↗
25. Li, J., Zhu, S., Yang, H.H., and Xu, J. (August, January 29). What does Artificial Intelligence Generated Content bring to Teaching and Learning? A literature review on AIGC in Education. Proceedings of the 2024 International Symposium on Educational Technology (ISET), Macao, China.10.1109/ISET61814.2024.00013 ↗
26. ChatGPT for good? On opportunities and challenges of large language models for education10.1016/j.lindif.2023.102274 ↗
27. Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11.10.3390/healthcare11060887 ↗
28. Chakrabarti, A., Tornatzky, L., and Fleischer, M. (1990). The Processes of Technological Innovation, Lexington Books.
29. Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals10.1016/j.ijmedinf.2014.12.004 ↗
30. The technology–organization–environment framework
31. I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents10.1016/j.tele.2020.101473 ↗
32. The impact of organizational context on the levels of cross-border e-commerce adoption in Chinese SMEs: The moderating role of environmental context10.3390/jtaer16070150 ↗
33. The influence of organizational culture on technology integration in teacher education
34. CALL teacher education as an impetus for L2 teachers in integrating technology10.1017/S095834400999019X ↗
35. Identifying organizational culture clash in MIS implementation: When is it worth the effort?10.1016/0378-7206(91)90041-Y ↗
36. A study of normative and informational social influences upon individual judgment10.1037/h0046408 ↗
37. Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making10.1016/j.technovation.2021.102312 ↗
38. An integrated TOE–DoI framework for cloud computing adoption in the higher education sector: Case study of Sub-Saharan Africa, Ethiopia10.1007/s13198-019-00872-z ↗
39. Towards development of an integrated cloud-computing adoption framework—A case of Indian school education system
40. QMOS: Query-based multi-documents opinion-oriented summarization10.1016/j.ipm.2017.12.002 ↗
41. Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features10.1016/j.neucom.2016.02.077 ↗
42. The opportunities and challenges of ChatGPT in education10.1080/10494820.2023.2253858 ↗
43. Yu, J.H., and Chauhan, D. (2024). Trends in NLP for personalized learning: LDA and sentiment analysis insights. Education and Information Technologies, Springer.10.1007/s10639-024-12988-2 ↗
44. Interpreting the public sentiment variations on twitter
45. Research on semi-automatic construction method of high-speed train
46. Turney, P.D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. arXiv.10.3115/1073083.1073153 ↗
47. Word cloud analysis of the BJGP10.3399/bjgp12X630142 ↗
48. Potential use of chat gpt in global warming10.1007/s10439-023-03171-8 ↗
49. Wang, J., Xia, Y., and Wu, Y. (2022). Sensing tourist distributions and their sentiment variations using social media: Evidence from 5A scenic areas in China. ISPRS Int. J. Geo-Inf., 11.10.3390/ijgi11090492 ↗
50. Fu, W., and Zhou, B. (2024). Theme Exploration and Sentiment Analysis of Online Reviews of Wuyishan National Park. Land, 13.10.3390/land13050629 ↗
51. Robot restaurant experience and recommendation behaviour: Based on text-mining and sentiment analysis from online reviews10.1080/13683500.2024.2309140 ↗
52. Latent dirichlet allocation
53. A density-based method for adaptive LDA model selection10.1016/j.neucom.2008.06.011 ↗
54. Ma, Y., Ling, C., and Wu, J. (2020). Exploring the spatial distribution characteristics of emotions of weibo users in wuhan waterfront based on gender differences using social media texts. ISPRS Int. J. Geo-Inf., 9.10.3390/ijgi9080465 ↗
55. Qualitative analysis of customer behavior in the retail industry during the COVID-19 pandemic: A word-cloud and sentiment analysis approach10.1016/j.jretconser.2023.103543 ↗
56. Improved word segmentation system for Chinese criminal judgment documents10.1080/08839514.2023.2297524 ↗
57. Chinese micro-blog sentiment analysis based on multiple sentiment dictionaries and semantic rule sets10.1109/ACCESS.2019.2960655 ↗
58. An exploratory study of information re-finding behaviour modes of Chinese college students on social media: Video diary analysis from Chinese platforms10.1080/0144929X.2024.2331261 ↗
59. Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence10.1016/j.compedu.2024.105071 ↗
60. Zhang, X., Zhu, F., Wang, K., Cao, G., Xue, Y., and Liu, M. (2024). Bring the Intelligent Tutoring Robots to Education: A Systematic Literature Review. IEEE Trans. Learn. Technol., 1–21.10.1109/TLT.2024.3428366 ↗
61. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy10.1016/j.ijinfomgt.2019.08.002 ↗
62. Toward understanding the impact of artificial intelligence on labor10.1073/pnas.1900949116 ↗
63. Artificial intelligence as augmenting automation: Implications for employment10.5465/amp.2019.0062 ↗
64. The role of emotions in student teachers’ professional identity10.1080/02619768.2012.662637 ↗
65. How should we change teaching and assessment in response to increasingly powerful generative Artificial Intelligence? Outcomes of the ChatGPT teacher survey
66. Reexamining the role of gifted education and talent development for the 21st century: A four-part theoretical approach10.1177/0016986212444901 ↗
67. Quality in e-learning: A framework for promoting and assuring quality in virtual institutions10.1111/j.1365-2729.2011.00440.x ↗
68. English learning websites and digital resources from the perspective of Chinese university EFL practitioners10.1017/S0958344014000263 ↗
69. Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions10.1016/j.jclepro.2023.140527 ↗
70. Artificial intelligence in education: The three paradigms10.1016/j.caeai.2021.100020 ↗
71. Generative AI, learning and new literacies10.18785/jetde.1602.01 ↗
72. Gökoğlu, S. (2024). Challenges and limitations of generative AI in education. Transforming Education with Generative AI: Prompt Engineering and Synthetic Content Creation, IGI Global.10.4018/979-8-3693-1351-0.ch008 ↗
73. Paper, T. (2023, August 02). Observation | Hong Kong Universities Are Embracing ChatGPT and Busy Preparing for New Semester Changes During the Summer Vacation. Available online: https://www.thepaper.cn/newsDetail_forward_24072878.
74. Zhou, J., and Zhang, H. (2024). Factors Influencing University Students’ Continuance Intentions towards Self-Directed Learning Using Artificial Intelligence Tools: Insights from Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis. Appl. Sci., 14.10.3390/app14188363 ↗
75. Strategic leadership for responsible artificial intelligence adoption in higher education10.55056/cte.616 ↗
76. Xing, B., and Marwala, T. (2017). Implications of the fourth industrial age on higher education. arXiv.10.25073/0866-773X/87 ↗
77. Wu, D., Zhang, X., Wang, K., Wu, L., and Yang, W. (2024). A multi-level factors model affecting teachers’ behavioral intention in AI-enabled education ecosystem. Educ. Technol. Res. Dev., 1–33.10.1007/s11423-024-10419-0 ↗
78. Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., and Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15.10.3390/info15100596 ↗
79. Developing an early-warning system through robotic process automation: Are intelligent tutoring robots as effective as human teachers?
80. Hacker, P., Engel, A., and Mauer, M. (2023, January 12–15). Regulating ChatGPT and other large generative AI models. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL, USA.10.1145/3593013.3594067 ↗
81. Aligning community outreach initiatives with SDGs in a higher education institution with artificial intelligence10.1016/j.clrc.2023.100160 ↗
82. Factors influencing initial public acceptance of integrating the ChatGPT-type model with government services10.1108/K-06-2023-1011 ↗
83. A systematic review of AI role in the educational system based on a proposed conceptual framework10.1007/s10639-021-10774-y ↗
84. Holmes, W., Bialik, M., and Fadel, C. (2019). Artificial Intelligence in Education Promises and Implications for Teaching and Learning, Center for Curriculum Redesign.
85. Castañeda, L., and Selwyn, N. (2018). More Than Tools? Making Sense of the Ongoing Digitizations of Higher Education, Springer.10.1186/s41239-018-0109-y ↗