Current Volume 8
Abstract
This paper presents an practical approach to increase student study preparation through the integration of multi modal AI systems and fuzzy search capabilities using Fuse.js. By incorporating natural lan-guage processing, fuzzy logic, and intelligent search mechanisms, the proposed system bridges the gap between individual learning methode and educational content. Leveraging new search and AI-driven personalization, this method significantly improves the efficiency and effectiveness of study practices. Empirical evaluation validate notable gains in learning outcomes compared to conventional study planning systems. The system is prepared to understand user intent from diverse inputs—text, voice, or image—and re-turn contextually relevant study materials with high precision. Through the use of fuzzy search, it ac-commodates imperfect queries and varying levels of subject understanding, allowing for a more flexi-ble and student-centric experience. This adaptability not only enhances the user interface but also pro-motes deeper engagement with the material. The framework shows promise for deployment in educa-tional platforms, tutoring systems, and self-paced learning environments, contributing to a more acces-sible and intelligent learning ecosystem.
Keywords
Multi-modal AI, Fuzzy Search, Natural Language Processing (NLP), study preparation, personalized Learning, Fuse.js, adaptive Learning
Citations
IRE Journals:
Amol Ajit Gugale
"A Multi-modal AI-Based Student Study Preparation System Using Fuzzy Search Implementation" Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 976-980
IEEE:
Amol Ajit Gugale
"A Multi-modal AI-Based Student Study Preparation System Using Fuzzy Search Implementation" Iconic Research And Engineering Journals, 8(11)
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