Digital Financial Inclusion and Its Impact on Economic Growth
The rapid growth of Generative Artificial Intelligence (GenAI) has made AI literacy an essential skill for students, yet conventional classroom and e-learning platforms provide limited support for helping learners understand, question, and critically evaluate AI-generated information. This paper presents an AI-Driven Instructional Support System with Dynamic Learning Analytics for Classroom Evaluation, a role-based, web-based educational platform branded EduMentor that integrates academic and assessment management with a Generative AI instructional-support layer. The system employs Retrieval-Augmented Generation (RAG) and web-scraping-based verification to ground AI responses in course material and trusted external sources, assigns a confidence score to each generated response, and classifies learning content using Bloom's Taxonomy to support progressive cognitive development from Remember and Understand to Apply, Analyze, Evaluate, and Create. A Django backend, a relational database, and a Flutter-based client provide role-specific interfaces for Administrator, HOD, Staff/Teacher, and Student users, along with assessment management, student-performance analysis, and a learning-analytics dashboard. The system was evaluated using 100 functional test cases spanning authentication, assessment management, performance analysis, AI-generated recommendations, and application communication. The platform achieved 94% overall functional accuracy, 100% authentication accuracy, 89% AI-recommendation relevance accuracy, a usability score of 4.4/5 (88%), and an average API response time of 1.8 seconds. The results indicate that an integrated role-based platform combining assessment data with AI-assisted instructional feedback is a feasible and practical approach for data-driven classroom evaluation, provided that AI-generated recommendations continue to be treated as decision-support output requiring teacher verification.