MUMBAI, India, Nov. 13 -- Intellectual Property India has published a patent application (202621118521 A) filed by Dr. D. Y. Patil Institute Of Technology, Pimpri, Pune; and Dnyaan Prasad Global University, School Of Technology And Research, Pimpri, Pune on October 04, 2026, for Ai-Powered Emotion-Adaptive Learning Ecosystem Using Multimodal Affective Analytics And Cognitive Digital Twin Modeling.
Inventors include Dr. Nilakshee Rajule; Dr. Mithra Venkatesan; Dr. Sheetal Pawar; Dr. Savita Jadhav; and Ms. Rajashree Bhokare.
The application for the patent was published on November 13, 2026, under issue no. 46/2026.
Abstract: The present invention discloses an AI-Powered Emotion-Adaptive Learning Ecosystem using Multimodal Affective Analytics and Cognitive Digital Twin Modeling for delivering personalized, intelligent, and adaptive educational experiences. The system integrates Artificial Intelligence, Affective Computing, Cognitive Digital Twin Technology, Neuro-Symbolic Pedagogical Intelligence, Generative Artificial Intelligence, and Explainable Learning Intelligence within a unified framework. A Multimodal Data Acquisition Layer collects learner information from EEG signals, facial expressions, eye-tracking systems, voice patterns, physiological sensors, and learning interaction logs. An Affective Analytics Layer processes and fuses the acquired data to determine learner emotions, engagement, cognitive load, attention, stress, and learning behavior. The analyzed information continuously updates a Cognitive Digital Twin, providing a dynamic representation of the learner’s emotional state, cognitive condition, learning preferences, performance, and knowledge mastery. A Neuro-Symbolic Pedagogical Engine utilizes the digital twin information to determine personalized instructional strategies, adaptive learning pathways, interventions, and pedagogical recommendations. A Generative AI Content Adaptation Layer dynamically modifies educational content, explanations, assessments, examples, and difficulty levels according to learner requirements. An Explainable Learning Intelligence Layer provides transparent learning insights, adaptation decisions, progress information, and personalized recommendations. A closed-loop feedback mechanism continuously evaluates learner responses and educational outcomes to improve subsequent adaptations. The proposed learner-centric ecosystem enhances engagement, motivation, knowledge retention, academic performance, and learning effectiveness and is applicable to schools, higher education, online learning platforms, corporate training, professional certification, and lifelong learning environments.
Disclaimer: Curated by HT Syndication.
