To accept or not to accept? An IRT-TOE Framework to Understand Educators' Resistance to Generative AI in Higher Education
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909272764317696 |
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| author | Kalmus, Jan-Erik Nikiforova, Anastasija |
| author_facet | Kalmus, Jan-Erik Nikiforova, Anastasija |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20130 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | To accept or not to accept? An IRT-TOE Framework to Understand Educators' Resistance to Generative AI in Higher Education Kalmus, Jan-Erik Nikiforova, Anastasija Computers and Society Artificial Intelligence Emerging Technologies Human-Computer Interaction Information Theory 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. |
| title | To accept or not to accept? An IRT-TOE Framework to Understand Educators' Resistance to Generative AI in Higher Education |
| topic | Computers and Society Artificial Intelligence Emerging Technologies Human-Computer Interaction Information Theory |
| url | https://arxiv.org/abs/2407.20130 |