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

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.