Prioritising Strategies to Improve Girls’ Access to Quality Education: A Hybrid Hyperbolic Fuzzy MCDM Framework with Human and AI Expertise
DOI:
https://doi.org/10.15837/ijccc.2025.6.7240Keywords:
Girls’ Education, Soft Cluster Rectangle, Hyperbolic Fuzzy Sets, Generative AI, Hybrid Decision Framework.Abstract
Access to quality education for girls is one of the key targets of the United Nations’ Sustainable Development Goals. This study has two major goals. First, this study proposes a hybrid decision framework that integrates hyperbolic fuzzy sets, multi-attitudinal variance, soft cluster rectangle, and integrated simple weighted sum product methods to prioritise strategies that improve access to quality education for girls. Second, this study explores the use of generative AI expertise in a multi-criteria decision-making framework that traditionally uses only human experts. The findings reveal "Effectiveness" to be the most important criterion when assessing the strategies and "Health and Hygiene Interventions" to be the most prioritised strategy. The findings also show a divergence of opinions between the AI experts and human experts in the extremes and alignment of opinions in the non-extremes. The study serves as a foundation for future research on integrating generative AI into decision-making frameworks while also providing policymakers with a structured approach to prioritising strategies for improving girls’ education.
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