AI-POWERED STUDENT ASSESSMENT: A CNN-DRIVEN APPROACH TO ACADEMIC MONITORING AND PARENT ENGAGEMENT - Kumar, Kapil; Himanshu; Sharma, Piyush; Madan, Shefali (International Journal of Research -GRANTHAALAYAH, 2024)
In the modern educational landscape, effective student monitoring and parental engagement are crucial for academic success. However, traditional approaches such as periodic parent-teacher meetings and paper-based reports often fail to provide timely and actionable insights. These limitations hinder parents from identifying their child’s learning gaps early and make it difficult for teachers to maintain consistent communication across large student populations. To address these challenges, the Student Assessment & Performance (SAP) Tracker leverages Convolutional Neural Networks (CNNs) to analyze and interpret student handwriting, scanned assignments, and exam sheets for automated performance evaluation. By integrating CNN-based image recognition with academic data, the system offers deeper insights into student behavior, comprehension patterns, and learning progress over time. This AI-enhanced assessment enables the early detection of academic struggles and fosters proactive intervention. Built using a client-server architecture, the SAP Tracker features a Flutter-based frontend and a secure backend, seamlessly integrated with a cloud-based database for real-time data synchronization. This infrastructure ensures scalability, low latency, and accessibility across platforms. The SAP Tracker empowers parents with immediate access to grades, attendance, assignment analytics, and school notifications, strengthening the home-school connection. Ultimately, the system enhances student outcomes through timely support, increased parental involvement, and data-driven educational insights.