Educator Perspective of Barriers to Generative AI Adoption in Estonian Higher Education Using an IRT-TOE Based Model
Name
Jan-Erik Kalmus
Abstract
The benefits and challenges associated with the adoption of Generative Artificial Intelligence (GenAI) in education have been a source of discussion ever since ChatGPT was made available to the public in late 2022. In the field of information systems, technology adoption research is used to understand the various factors that affect the adoption of a certain technology. Theoretical models, which have been developed and empirically validated over decades, are used to gain an understanding of individual or organisational requirements, challenges, and perception related to the adoption of a given technology. As educators play a critical role in enabling the use of GenAI in higher education, this thesis aims to uncover the negative factors affecting educators’ decision to allow students use Generative Artificial Intelligence (GenAI) in Estonian higher education. This is done by first performing a systematic literature review, followed by the development of a theoretical model based on constructs of Innovation Resistance Theory (IRT) and Technology-Organization-Environment (TOE) framework. The novel theoretical model is later tested with a survey based quantitative methodology that involves 149 participants in various Estonian universities. The results of this analysis shows that educators in Estonian higher education have generally accepted the use of GenAI in their courses, while academic fraud and the effect on students’ critical thinking skills remain primary points of concern. Furthermore, the results highlight that resistance to student GenAI use is associated with challenges in evaluation and skepticism towards the value the use of this technology brings to their courses. The perspective of resistance is generally neglected in research and existing research has generally focused on the adoption of ChatGPT by students, investigating factors that contribute to the acceptance but not barriers that prevent adoption of the technology in question. The study contributes to the evolution of technology adoption research by introducing a novel approach to evaluate resistance to technology based on individual, environmental and organisational factors, which can be used in other settings to evaluate resistance to GenAI adoption.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Anastasija Nikiforova
Defence year
2024