Comparative analysis of multi-criteria decision making methods for prioritizing influential factors of ChatGPT adoption in higher education

Abstract

Various Multi-Criteria Decision-Making (MCDM) methods are analyzed to identify key factors influencing the adoption of ChatGPT in higher education. The research focuses on understanding the factors affecting ChatGPT adoption in educational settings, to provide a comprehensive framework to evaluate and rank these factors, including usage, agent, technical, and trust-related considerations. To achieve this, the study employs techniques such as the Weighted Sum Model (WSM), Shapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). The methodology involves surveying 55 students from a mechanical engineering program, initially using conventional research methods and later integrating ChatGPT into their workflow. The results, derived from comparing both approaches, provide insights into the practical implications of integrating ChatGPT in education. The findings highlight the importance of a user-friendly interface, multimodal communication, security, emphasizing the need for accessible, intuitive platforms to promote adoption and engagement. Educational institutions should focus on simplifying navigation and enhancing accessibility, ensuring that both students and educators, especially those with limited technical experience, can easily interact with ChatGPT. This study offers valuable insights into the complexities of ChatGPT adoption, helping institutions develop customized strategies, effective implementation strategies. The results suggest that integrating ChatGPT into educational settings brings significant benefits, underscoring the need for a strategic, context-specific approach to its adoption.