Intellectual Property India Publishes Patent Application for A Medai-Based Intelligent Adaptive Learning And Automated Quiz Generation System For Medical Education

MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202611060344 A) filed by Greater Noida Institute Of Technology Engineering Institute; Mr. Harendra Singh; Mr. Pankaj Chopra; Dr. Shweta Rai; Ms. Akanksha; Shraddha Tiwari; Shishir Kumar Tiwari; Shivam Kumar; Shekhar Kumar; Sonu Kumar; Vikas Thakur; Sahil Raj Sinha; Roushan Kumar Pandey; Vishwajit Kumar Verma; Suraj Kumar Singh; Ravi Kumar Mishra; Tushar Chauhan; Shilajeet; Vikash Kumar; and Uttam Kumar on May 12, 2026, for A Medai-Based Intelligent Adaptive Learning And Automated Quiz Generation System For Medical Education.

Inventors include Mr. Harendra Singh; Mr. Pankaj Chopra; Dr. Shweta Rai; Ms. Akanksha; Shraddha Tiwari; Shishir Kumar Tiwari; Shivam Kumar; Shekhar Kumar; Sonu Kumar; Vikas Thakur; Sahil Raj Sinha; Roushan Kumar Pandey; Vishwajit Kumar Verma; Suraj Kumar Singh; Ravi Kumar Mishra; Tushar Chauhan; Shilajeet; Vikash Kumar; and Uttam Kumar.

The application for the patent was published on July 17, 2026, under issue no. 29/2026.

Abstract: ABSTRACT Disclosed herein is a MedAI-based intelligent adaptive learning and automated quiz generation system (100) for medical education, the system (100) comprises a user interface module (102) configured to enable registration, authentication, and interaction of a plurality of users. A data acquisition module (104) configured to collect learner-specific data. A learner modeling module (106) configured to generate and dynamically update a personalized learner profile. An intelligent content management module (108) configured to store, organize, and retrieve medical educational content. An artificial intelligence-based adaptive learning engine (110) configured to analyze the personalized learner profile. An automated quiz generation module (112) configured to generate customized assessments in real time. An evaluation and feedback module (114) configured to assess user responses. A continuous learning and optimization module (116) configured to update the learner modeling module (106) and refine the artificial intelligence-based adaptive learning engine (110).

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