Comparative Analysis of Mcdm Methods for Prioritizing Influential Factors of Chatgpt Adoption in Higher Education

Abstract

This study comprehensively analyzes various multi-criteria decision making (MCDM) methods for prioritizing influential factors in adopting ChatGPT within higher education. By employing techniques such as the Weighted Sum Model (WSM), Shapley Additive explanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME), the research aims to provide a robust framework for assessing and ranking variables that impact the integration of ChatGPT. A structured approach involving surveys thoroughly evaluated factors related to usage, agents, technical aspects, and trust. The outline of our methodology commences with the use of conventional research methods by 55 students enrolled in a manufacturing process course within a mechanical engineering program, which will then integrate ChatGPT into their workflow and culminate in a comprehensive comparison and discussion of the results derived from both approaches. This systematic exploration is geared towards revealing profound insights into the evolving interplay between education and technology integration. The findings indicate distinct prioritizations across the methods, highlighting each approach's strengths and contextual benefits in decision-making processes. This in-depth analysis sheds light on the diverse considerations for ChatGPT adoption, making the audience aware of the complexity and nuances of the topic. It offers strategic insights for educational institutions to develop more robust implementation strategies tailored to their needs and contexts.

Keywords: Multi-criteria decision making ChatGPT WSM SHAP LIME