MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082698 A) filed by Madhankumar C; and Mr Ajay Bhausaheb Jadhav on July 05, 2026, for Ai-Driven Intelligent Academic Performance Analytics And Personalized Learning Enhancement System For Higher Education Institutions.
Inventor includes Mr Ajay Bhausaheb Jadhav.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: AI-Driven Intelligent Academic Performance Analytics and Personalized Learning Enhancement System for Higher Education Institutions Abstract The rapid digital transformation of higher education has generated vast amounts of academic, behavioral, and institutional data, creating new opportunities to improve student success through intelligent analytics. This invention proposes an AI- Driven Intelligent Academic Performance Analytics and Personalized Learning Enhancement System that leverages Artificial Intelligence (AI), Machine Learning (ML), Learning Analytics, and predictive modeling to monitor, analyze, and enhance student academic performance in higher education institutions. The system integrates data from Learning Management Systems (LMS), attendance records, internal assessments, assignment submissions, examination results, classroom engagement, and extracurricular activities to develop comprehensive student learning profiles. Advanced machine learning algorithms analyze historical and real-time educational data to predict academic outcomes, identify at-risk students, detect learning gaps, and recommend personalized learning pathways tailored to individual strengths and weaknesses. The system employs explainable AI techniques to provide transparent recommendations for students, faculty members, academic advisors, and administrators. Intelligent dashboards visualize academic progress, competency mapping, course performance, and institutional analytics to support data-driven academic decision-making. The proposed framework further incorporates adaptive learning mechanisms that dynamically recommend study materials, quizzes, remedial sessions, mentoring opportunities, and skill-development programs based on each learner's performance trends and learning behavior. Real-time alerts notify stakeholders regarding declining academic performance, attendance issues, and potential dropout risks, enabling timely intervention strategies. Cloud-based deployment ensures scalability, secure data storage, and seamless integration with existing educational platforms while maintaining data privacy through encryption and role-based access control. By combining predictive analytics, personalized learning recommendations, and intelligent institutional reporting, the proposed system enhances student engagement, improves academic achievement, supports faculty in evidence based teaching, and assists administrators in optimizing educational quality. The invention contributes to the development of intelligent, student-centric higher education ecosystems capable of delivering personalized, adaptive, and data driven learning experiences that improve educational outcomes and institutional excellence.
Disclaimer: Curated by HT Syndication.
