Date
Publisher
arXiv
Since the public release of Chat Generative Pre-Trained Transformer (ChatGPT), extensive discourse has emerged concerning the potential advantages and challenges of integrating Generative Artificial Intelligence (GenAI) into education. In the realm of information systems, research on technology adoption is crucial for understanding the diverse factors influencing the uptake of specific technologies. Theoretical frameworks, refined and validated over decades, serve as guiding tools to elucidate the individual and organizational dynamics, obstacles, and perceptions surrounding technology adoption. However, while several models have been proposed, they often prioritize elucidating the factors that facilitate acceptance over those that impede it, typically focusing on the student perspective and leaving a gap in empirical evidence regarding educators viewpoints. Given the pivotal role educators play in higher education, this study aims to develop a theoretical model to empirically predict the barriers preventing educators from adopting GenAI in their classrooms. Acknowledging the lack of theoretical models tailored to identifying such barriers, our approach is grounded in the Innovation Resistance Theory (IRT) framework and augmented with constructs from the Technology-Organization-Environment (TOE) framework. This model is transformed into a measurement instrument employing a quantitative approach, complemented by a qualitative approach to enrich the analysis and uncover concerns related to GenAI adoption in the higher education domain.
Study at a glance
- Question: What factors and barriers influence higher education educators' resistance to adopting generative AI tools in their courses?
- Setting and data: Higher education settings; framework development paper without empirical participant data reported (application ongoing in Estonia).
- Method: Theoretical framework and measurement instrument development combining quantitative survey items and qualitative questions, grounded in literature review and expert reviews.
Why it matters
Research on generative AI in education predominantly highlights student adoption and facilitating factors. Understanding educators' perspectives and the barriers driving their resistance is crucial for promoting responsible adoption in higher education.
Key findings
- Prior research heavily centers on student technology acceptance rather than resistance, largely focusing on ChatGPT rather than a wider variety of generative AI tools.
- A theoretical model integrating Innovation Resistance Theory with organizational and environmental dimensions identifies tradition, image, risk, usage, value, organizational, and environmental barriers to educators' adoption.
- A 31-item measurement instrument with complementary open-ended questions was developed and refined through iterative expert review to empirically assess educators' resistance to generative AI.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
