# Modeling generative AI adoption and digital divide among Chinese rural teachers > Source: > Published: 2026-08-03 19:38:44+00:00 ## Abstract 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* < 0.01), followed by perceived usefulness (β = 0.509, *p* < 0.01) and perceived ease of use (β = 0.293, *p* < 0.01). The model explained 57.3% of the variance in attitudes and 32.3% of the variance in self-reported usage behavior. Digital divide conditions significantly moderated the association between perceived ease of use and attitude, whereas moderation was not supported for the other direct paths to attitude. Teachers facing lower digital divide conditions also reported broader and more pedagogically advanced uses of GenAI. These findings indicate that GenAI adoption in rural schools is associated not only with perceived usefulness and ease of use but also with the resource conditions that support meaningful use, underscoring the need for differentiated policy interventions that prioritize technical support infrastructure and address multi-dimensional digital inequalities in rural educational settings. ## Acknowledgements The authors thank the participating teachers and school administrators for their time and cooperation in this study. ## Funding 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 ### Authors and Affiliations ### Corresponding author ## Ethics declarations ### Competing interests 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. ### Consent for publication Not applicable. This manuscript does not contain any identifiable personal data, images, or other information that would require consent for publication. ## Additional information ### Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ## Supplementary Information Below is the link to the electronic supplementary material. ## Rights and permissions **Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/). ## About this article ### Cite this article Xin, Y., Shusheng, D., Weina, H. *et al.* Modeling generative AI adoption among rural teachers: the moderating role of the digital divide in resource-constrained contexts. *Sci Rep* (2026). https://doi.org/10.1038/s41598-026-64624-3 Received: Accepted: Published: DOI: https://doi.org/10.1038/s41598-026-64624-3