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Integrating AI and Fuzzy Systems to Enhance Education Equity |
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PP: 403-422 |
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doi:10.18576/amis/190215
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Author(s) |
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Suleiman Ibrahim Mohammad,
N. Yogeesh,
N. Raja,
P. William,
M. S. Ramesha,
Asokan Vasudevan,
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Abstract |
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| In this study, a complete fuzzy-based framework was developed for balanced allocation of resources in seamless schools in line with Sustainable Development Goal (SDG 4). The study simplifies the uncertainty and complexity of educational data by combining Fuzzy Rule-Based Systems (FRBS), Fuzzy Cognitive Maps (FCM) and Fuzzy Multi-Criteria Decision-Making (MCDM) methods. The framework uses enrolment, access and quality as the three factors to prioritise schools for targeted interventions and allocation of better resources. The approach leverages the composition of expert-driven rules, causal modelling, and quantitative ranking to identify which schools need the most attention and allocate resources accordingly in real-time. The results from the case study showed that schools that continuously scored poorly in every criterion received the highest priority while schools performing moderately or better received an equitable allocation of resources. The results highlight the need for data-informed leadership in tackling education disparities into manifest, practical recommendations for policy makers. Future lines of research pointed out are the addition of other criteria, the prediction by means of machine learning and the broadening the framework into other contexts. This research adds a scalable, sustained model for equitable educational development around the globe.
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