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Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance.

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Title: Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance.
Authors: Pasupuleti, Ravi Sankar1, Jangam, Deevena Charitha2, Appana, Sai Manideep3, Nalluri, Venkateswarlu4 nallurivenkey7@gmail.com, Thiyyagura, Deepthi5
Source: Contemporary Educational Technology. Jan2026, Vol. 18 Issue 1, p1-17. 17p.
Subject Terms: *Artificial intelligence, *Expectation (Psychology), *Higher education, *Teacher education, Structural equation modeling, Technology Acceptance Model, Innovation adoption
Geographic Terms: India
Abstract: The advent of artificial intelligence (AI) has had a profound impact on the education sector, resulting in a transformative change in higher education worldwide. One such change is the usage of AI tools by teachers to enhance their teaching practices, including content creation, sharing, and personalized learning. Those certain obstacles persist for teachers while fully exploring the potential of AI and its adoption in teaching practices. An extensive review of the literature revealed a significant research gap in developing a comprehensive study to examine the influence of AI relevance and its readiness, performance expectancy (PE), and effort expectancy (EE) in shaping behavioral intention (BI) for AI adoption in teaching. Therefore, drawing cues from the unified theory of acceptance and use of technology a research framework was developed to examine these intricate relationships. We gathered data by administering a survey to higher education teachers across various educational organizations in India. Structural equation modeling (SEM) was employed to analyze the collected data and test the hypothesized relationships. The results uncovered a positive association between teacher's perceptions of AI's relevance and their readiness to adopt AI, with both factors positively influencing their BI. Furthermore, this study found that EE exhibited a significant positive effect on both BI and PE. This study discusses theoretical and practical implications, underscoring the importance of raising awareness about AI's relevance, and lays the groundwork for further exploration in this emerging area, intending to inform strategies and interventions to support successful AI adoption in educational organizations. [ABSTRACT FROM AUTHOR]
Copyright of Contemporary Educational Technology is the property of Bastas Publications 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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  Data: The advent of artificial intelligence (AI) has had a profound impact on the education sector, resulting in a transformative change in higher education worldwide. One such change is the usage of AI tools by teachers to enhance their teaching practices, including content creation, sharing, and personalized learning. Those certain obstacles persist for teachers while fully exploring the potential of AI and its adoption in teaching practices. An extensive review of the literature revealed a significant research gap in developing a comprehensive study to examine the influence of AI relevance and its readiness, performance expectancy (PE), and effort expectancy (EE) in shaping behavioral intention (BI) for AI adoption in teaching. Therefore, drawing cues from the unified theory of acceptance and use of technology a research framework was developed to examine these intricate relationships. We gathered data by administering a survey to higher education teachers across various educational organizations in India. Structural equation modeling (SEM) was employed to analyze the collected data and test the hypothesized relationships. The results uncovered a positive association between teacher's perceptions of AI's relevance and their readiness to adopt AI, with both factors positively influencing their BI. Furthermore, this study found that EE exhibited a significant positive effect on both BI and PE. This study discusses theoretical and practical implications, underscoring the importance of raising awareness about AI's relevance, and lays the groundwork for further exploration in this emerging area, intending to inform strategies and interventions to support successful AI adoption in educational organizations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Contemporary Educational Technology is the property of Bastas Publications 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.</i> (Copyright applies to all Abstracts.)
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              Text: Jan2026
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