An AI-Based Smart Classroom Assessment and Academic Intelligence System for Real-Time Learning Analytics
DOI:
https://doi.org/10.15662/IJEETR.2026.0802033Keywords:
Smart Classroom Systems, Generative Artificial Intelligence, Learning Analytics, Academic Intelligence, Automated Assessment, AI in Education, Real-Time Evaluation, Predictive Learning SystemsAbstract
Modern educational environments must support highly diverse academic requirements such as real-time student assessment, personalized learning, and data-driven decision-making. Conventional classroom systems are designed to function across general teaching scenarios, but they lack the adaptability required to evaluate student understanding instantly and effectively. To address these limitations, intelligent academic systems, referred to as Smart Academic Networks (SmartEdu Systems), have been introduced. These systems incorporate cognitive capabilities that enable dynamic adaptation of assessment mechanisms based on classroom context and student performance. Recent advancements in Artificial Intelligence and Generative AI serve as a key enabler for such systems by supporting automated content generation and continuous learning. By integrating AI functionalities, the proposed system leverages an AI-driven assessment engine capable of generating topic-based quizzes and analyzing student responses in real time. In this project, an AI-based academic intelligence system is proposed for classroom-level evaluation and performance analytics. The system learns patterns from academic data such as quiz scores, attendance, and assignment submissions to provide meaningful insights into student performance. The proposed approach enables intelligent assessment, early identification of weak learners, and adaptive feedback for improved learning outcomes. Performance evaluation of the prototype demonstrates enhanced teaching efficiency, reduced manual workload, and improved student engagement compared to traditional assessment methods. The results highlight the effectiveness, adaptability, and scalability of AI-driven academic systems in modern educational environments
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