MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641112857 A) filed by Dr. Ramesh Shahabadkar on September 21, 2026, for Artificial Intelligence-Based Adaptive Learning System For Personalized Prediction And Intelligent Recommendations.
Inventors include Dr. Ramesh Shahabadkar; Dr. Nandeeshwar S B; Dr. Krutika Ramesh Shahabadkar; Pallavi K V; Dr. Niranjan Kumar; and Dayanand Sagar Academy Of Technology And Management, Bengaluru.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: The present invention relates to an artificial intelligence-based adaptive learning system configured to generate personalized learning predictions and intelligent recommendations for individual users. The system integrates learner interaction data, academic performance data, assessment responses, learning behavior, temporal activity patterns, content preferences, competency indicators, and feedback data to construct a dynamically updating learner profile. An artificial intelligence engine comprising one or more machine-learning models processes the learner profile and determines predicted learning outcomes, competency gaps, probable performance, learning progression, and content requirements. A recommendation engine dynamically selects and prioritizes learning resources, assessments, revision activities, difficulty levels, learning sequences, and intervention strategies according to the predicted requirements of the learner. The system continuously receives subsequent learner responses and behavioral feedback and modifies the learner profile and recommendation parameters using an adaptive feedback mechanism. A prediction module can further estimate performance trends and identify learners requiring additional support. A recommendation-ranking mechanism evaluates relevance, learner competency, historical effectiveness, content difficulty, learning objectives, and interaction history to generate personalized recommendations. The system thereby provides a closed-loop artificial intelligence framework in which prediction, personalization, recommendation, learner interaction, and model adaptation are continuously integrated. The invention is applicable to educational platforms, professional training systems, skill-development environments, intelligent tutoring systems, and other computer-implemented learning environments requiring adaptive and individualized decision support.
Disclaimer: Curated by HT Syndication.
