MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202641109844 A) filed by Thulasimani T; Aenugu Shivajyothi; Ms. Antonette Monica Ignatius; Dr. M. Sridharan; Dr. Indhumathi M; Dr. Anita Pandey; Mrs. R. Madhumitha; Priti Kandewar; Ms. Mahabooba M; Dr. Shiv Narayan; Anto Gracious L A; and Dr. K. Sundareswari on September 13, 2026, for Ai-Enabled Academic Risk Prediction And Student Development Support System.
Inventors include Thulasimani T; Aenugu Shivajyothi; Ms. Antonette Monica Ignatius; Dr. M. Sridharan; Dr. Indhumathi M; Dr. Anita Pandey; Mrs. R. Madhumitha; Priti Kandewar; Ms. Mahabooba M; Dr. Shiv Narayan; Anto Gracious L A; and Dr. K. Sundareswari.
The application for the patent was published on October 02, 2026, under issue no. 40/2026.
Abstract: AI-Enabled Academic Risk Prediction and Student Development Support System is the proposed invention. The proposed invention is an AI-enabled academic risk prediction and student development support system for early identification of students who may experience academic difficulties and for providing personalized developmental interventions. The system collects authorized student data comprising academic performance, attendance, examination results, assignment completion, learning engagement, course-wise performance, and learning-management-system activities. The collected data is pre-processed and provided to a TabPFN 2.5-based tabular foundation model for identifying complex relationships among student attributes and generating an individualized academic-risk probability. Based on the probability, students are categorized into different risk levels. An Explainable AI (XAI) module determines the principal factors contributing to the predicted risk, while a personalized intervention engine generates suitable recommendations including remedial learning, mentoring, study planning, additional learning resources, and academic support. The system further monitors student progress and intervention outcomes to update the student profile and refine subsequent predictions and recommendations. Thus, the invention provides an integrated prediction, explanation, intervention, and feedback mechanism for proactive academic risk management and continuous student development.
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