Beyond the black box: How fuzzy logic and multi-modal AI are revolutionizing personalized education
Abstract
In classrooms around the world, educators are drowning in data—but starving for insight. Quiz scores, video engagement, homework submissions, and login timestamps: all are logged, analyzed, and visualized. Yet these numbers rarely answer the deeper questions. Why is a student struggling? What kind of support do they need? Most educational AI systems treat data points as isolated facts, ignoring the tangled web of factors that shape learning.
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
1. Wang D, Chen G. Making AI accessible for STEM teachers: Using explainable AI for unpacking classroom discourse analysis. IEEE Transactions on Education. 2024; 67(6): 907-918. doi: 10.1109/TE.2024.3421606
2. Chango W, Lara JA, Cerezo R, Romero C. A review on data fusion in multimodal learning analytics and educational data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2022; 12(4): e1458. doi: 10.1002/widm.1458
3. Li F, Li Y, Shang C, Shen Q. Fuzzy knowledge-based prediction through weighted rule interpolation. IEEE Transactions on Cybernetics. 2020; 50(10): 4508-4517. doi: 10.1109/TCYB.2018.2887340
4. Ahmad K, Iqbal W, El-Hassan A, et al. Data-driven artificial intelligence in education: A comprehensive review. IEEE Transactions on Learning Technologies. 2023; 17: 12-31. doi: 10.1109/TLT.2023.3314610
