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Fuzzy best-worst method for analysing the threats of AI in education

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  • Irene Orayag Mamites

Abstract

AI integration to support functional operations has been a growing interest in the literature owing to the numerous perceived and actual benefits. While this has been widely considered in several industries, integrating AI in education (AIEd) has been limited primarily due to diverse requirements in the educational platform. As such, it is difficult to pinpoint the threat that hampers such innovation. To provide an analytical framework to analyse the threats of applying AIEd, this paper employs the fuzzy best-worst method (BWM). To illustrate the framework, a case study in a state university in the Philippines is conducted. Interesting results revealed that the stakeholders prioritise threats related to the knowledge-based implementation of AI technologies, followed by threats related to the evaluation of the type of technologies available. Such results provide a guideline to stakeholders to address high-priority threats before integrating AIEd.

Suggested Citation

  • Irene Orayag Mamites, 2026. "Fuzzy best-worst method for analysing the threats of AI in education," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 33(5), pages 1-18.
    • Handle: RePEc:ids:ijmore:v:33:y:2026:i:5:p:1-18
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