An exploration of the key determinants for the application of AI-enabled higher education based on a hybrid Soft-computing technique and a DEMATEL approach

Abstract

The application of AI in higher education has greatly increased globally in the dynamic digital age. The adoption of developmentally appropriate practices using AI-enabled techniques for facilitating the performance of teaching and learning in the higher education domain is thus a necessary task, especially in the COVID 19 pandemic era. The development and implementation of such techniques involve many factors and are related to the classical multiple criteria decision-making (MCDM) issue; however, these factors surrounding supervisors will confuse them and may result in misjudgment. To clarify the relevant issues and illustrate the cause-and-effect relationships among factors, a hybrid soft-computing technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) and a DEMATEL approach was proposed in this study, which can help decision makers capture the best model necessary for achieving aspiration-level in a higher education management strategy. In the results submitted, the improvement priority for dimensions is based on the measurement of the influences, running in order of tutors for learners (A), skills and competences (B), interaction data to support learning (C), and universal access to global classrooms (D), and which can serve as a reference for the plan of AI-enabled teaching/learning for higher education.