Understanding School Readiness Factors in relation to the Incorporation of Artificial Intelligence using TOE Framework: An Empirical Evidence from India.
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| Title: | Understanding School Readiness Factors in relation to the Incorporation of Artificial Intelligence using TOE Framework: An Empirical Evidence from India. |
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| Authors: | Karan, Bablu1 (AUTHOR) peterkaran50@gmail.com, Angadi, G. R.1 (AUTHOR) |
| Source: | TechTrends: Linking Research & Practice to Improve Learning. Jan2025, Vol. 69 Issue 1, p38-59. 22p. |
| Subject Terms: | *Artificial intelligence, *Readiness for school, *Secondary schools, Convenience sampling (Statistics), Structural equation modeling |
| Abstract: | The integration of artificial intelligence (AI) in educational setting is rapidly growing. The huge impact of AI in education draws attention of school stakeholders to start incorporating AI in teaching and learning. AI in school curriculum is increasingly getting significant. The study aimed to understand factors influencing the incorporation of AI at secondary school context. With this aim, the study used popular theory of technology organisation environment (TOE) framework to develop research model and hypotheses. A total of 506 secondary teachers comprised the sample of the study. Data were obtained on 5-point rating scale using convenience sample technique. Structural equation modeling (SEM) was performed to test research hypotheses based on 479 useable responses. The results revealed that factors, relative advantage, compatibility, technical capability, management support, regulatory environment, show significantly positive influence on the incorporation of AI. But the factor, normative pressure demonstrates not significant. The findings validate the applicability of the TOE-based research model to understand factors in relation to AI incorporation at secondary school context. The model can help decision makers to facilitate AI incorporation in secondary school. [ABSTRACT FROM AUTHOR] |
| Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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