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Attitude Mining Toward Generative Artificial Intelligence in Education: The Challenges and Responses for Sustainable Development in Education

Author

Listed:
  • Yating Wen

    (School of Public Administration, Yanshan University, Qinhuangdao 066004, China)

  • Xiaodong Zhao

    (School of Public Administration, Yanshan University, Qinhuangdao 066004, China)

  • Xingguo Li

    (Graduate School, Yanshan University, Qinhuangdao 066004, China)

  • Yuqi Zang

    (School of Public Administration, Yanshan University, Qinhuangdao 066004, China)

Abstract

Generative artificial intelligence (GenAI) technologies based on big language models are becoming a transformative power that reshapes the future shape of education. Although the impact of GenAI on education is a key issue, there is little exploration of the challenges and response strategies of GenAI on the sustainability of education from a public perspective. This data mining study selected ChatGPT as a representative tool for GenAI. Five topics and 14 modular semantic communities of public attitudes towards using ChatGPT in education were identified through Latent Dirichlet Allocation (LDA) topic modeling and the semantic network community discovery process on 40,179 user comments collected from social media platforms. The results indicate public ambivalence about whether GenAI technology is empowering or disruptive to education. On the one hand, the public recognizes the potential of GenAI in education, including intelligent tutoring, role-playing, personalized services, content creation, and language learning, where effective communication and interaction can stimulate users’ creativity. On the other hand, the public is worried about the impact of users’ technological dependence on the development of innovative capabilities, the erosion of traditional knowledge production by AI-generated content (AIGC), the undermining of educational equity by potential cheating, and the substitution of students by the passing or good performance of GenAI on skills tests. In addition, some irresponsible and unethical usage behaviors were identified, including the direct use of AIGC and using GenAI tool to pass similarity checks. This study provides a practical basis for educational institutions to re-examine the teaching and learning approaches, assessment strategies, and talent development goals and to formulate policies on the use of AI to promote the vision of AI for sustainable development in education.

Suggested Citation

  • Yating Wen & Xiaodong Zhao & Xingguo Li & Yuqi Zang, 2025. "Attitude Mining Toward Generative Artificial Intelligence in Education: The Challenges and Responses for Sustainable Development in Education," Sustainability, MDPI, vol. 17(3), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1127-:d:1580398
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    References listed on IDEAS

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    1. Ibrahim Mutambik, 2024. "The Use of AI-Driven Automation to Enhance Student Learning Experiences in the KSA: An Alternative Pathway to Sustainable Education," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    2. Lian, Ying & Tang, Huiting & Xiang, Mengting & Dong, Xuefan, 2024. "Public attitudes and sentiments toward ChatGPT in China: A text mining analysis based on social media," Technology in Society, Elsevier, vol. 76(C).
    3. Sudhir Rana, 2023. "AI and GPT for Management Scholars and Practitioners: Guidelines and Implications," FIIB Business Review, , vol. 12(1), pages 7-9, March.
    4. Danah Henriksen & Punya Mishra & Rachel Stern, 2024. "Creative Learning for Sustainability in a World of AI: Action, Mindset, Values," Sustainability, MDPI, vol. 16(11), pages 1-20, May.
    5. Lynn J Frewer & Chaya Howard & Richard Shepherd, 1998. "Understanding public attitudes to technology," Journal of Risk Research, Taylor & Francis Journals, vol. 1(3), pages 221-235, July.
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