MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202611104723 A) filed by Prof. Dr. Shivpal Singh; Dr. Neelam Tandon; Dr. Sagarika Saha; Mr. Reshabh Dev; Dr. Sharmila Arunkumar; Dr. Shabana Azami; Dr. Smrita Jain; Ms. Aditi Vishnoi; Dr. Himani Grewal; Dr. Gaurav Gupta; Dr. Nishant Gupta; Dr. Amrit Kumar Agrawal; and Dr. Kaushik Shandilya on August 31, 2026, for Ai-Driven Holistic Classroom Intelligence System For Multidimensional Student Development Using Behavioral Analytics, Learning Data Mining, And Adaptive Educational Computing Framework.
Inventors include Prof. Dr. Shivpal Singh; Dr. Neelam Tandon; Dr. Sagarika Saha; Mr. Reshabh Dev; Dr. Sharmila Arunkumar; Dr. Shabana Azami; Dr. Smrita Jain; Ms. Aditi Vishnoi; Dr. Himani Grewal; Dr. Gaurav Gupta; Dr. Nishant Gupta; Dr. Amrit Kumar Agrawal; and Dr. Kaushik Shandilya.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: ABSTRACT The present invention discloses an AI-Driven Holistic Classroom Intelligence System for Multidimensional Student Development using behavioral analytics, learning data mining, and adaptive educational computing. The system integrates educational data obtained from learning management systems, student information systems, assessment platforms, attendance systems, digital learning resources, classroom interaction platforms, teacher inputs, and other authorized sources. A learning data mining engine extracts academic, temporal, sequential, behavioral, engagement, and assessment patterns, while a behavioral analytics engine determines learning and participation indicators. An artificial intelligence-based Student Intelligence Engine generates a dynamic Student Holistic Intelligence Profile representing multiple dimensions including academic competency, conceptual mastery, engagement, participation, collaboration, problem-solving, creativity, learning autonomy, communication, and developmental progression. An adaptive educational computing engine uses the profile to generate personalized learning resources, assessments, interventions, and learning pathways. An early-warning engine identifies potential learning gaps or declining engagement, while an intervention management module measures intervention effectiveness. A feedback mechanism continuously updates recommendations based on subsequent learning outcomes, thereby establishing a closed-loop adaptive educational intelligence framework. The invention enables personalized learning, proactive academic support, teacher decision support, continuous developmental monitoring, and holistic student development.
Disclaimer: Curated by HT Syndication.
