Modeling generative AI adoption among rural teachers: the moderating role of the digital divide in resource-constrained contexts | Scientific Reports

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Modeling generative AI adoption among rural teachers: the moderating role of the digital divide in resource-constrained contexts

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Abstract<br>Generative artificial intelligence (GenAI) is entering schools rapidly, but its uptake may be uneven in under-resourced settings. This study examined factors associated with rural teachers’ GenAI adoption in Guangxi, China, with particular attention to whether digital divide conditions alter key adoption pathways. Drawing on the Contextually Calibrated Technology Adoption Model (CCTAM)—a theoretically motivated re-parameterization of the Technology Acceptance Model that foregrounds resource dependency and multi-dimensional digital inequality as central components—we analyzed survey data from 971 rural teachers using structural equation modeling and moderation analysis. Technical support showed the strongest association with attitudes toward GenAI adoption (β = 0.515, p p p

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Education

Information systems and information technology

Science, technology and society

Acknowledgements<br>The authors thank the participating teachers and school administrators for their time and cooperation in this study.

Funding<br>This research was supported by the Guangxi Colleges and Universities Humanities and Social Sciences Key Research Base Fund (Project No. 22JDB03), the Guangxi Philosophy and Social Science Planning Research Project (Project No. 21FKS028), and the Guangxi Zhuang Autonomous Region New Medical Research and Practice Project (Project No. XYK202318).

Author information<br>Authors and Affiliations<br>Guangxi Medical University, Nanning, 530021, China<br>Yang Xin & Deng Yan

Guangxi Vocational and Technical College, Nanning, 530226, China<br>Deng Shusheng

Guangxi University of Finance and Economics, Nanning, 530007, China<br>Hu Weina

AuthorsYang XinView author publications<br>Search author on:PubMed Google Scholar

Deng ShushengView author publications<br>Search author on:PubMed Google Scholar

Hu WeinaView author publications<br>Search author on:PubMed Google Scholar

Deng YanView author publications<br>Search author on:PubMed Google Scholar

Corresponding author<br>Correspondence to<br>Hu Weina.

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The authors declare no competing interests.

Ethical approval and consent to participate

This study was approved by the Ethics Committee of Guangxi University of Finance and Economics (Approval No. 20240226). All procedures involving human participants were performed in accordance with relevant institutional and national ethical standards. Informed consent was obtained from all participants prior to data collection. Participation was entirely voluntary, and respondents were informed of their right to decline participation or withdraw at any time without penalty.

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Cite this article<br>Xin, Y., Shusheng, D., Weina, H. et al. Modeling generative AI adoption among rural teachers: the moderating...