Intellectual Property India Publishes Patent Application for Artificial Intelligence Framework For Personalized English Reading And Writing Skill Optimization Using Adaptive Learning Techniques

MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641067880 A) filed by T Sunandha Tulasi; Shalini R; Maganti Sushanth Babu; Siddunuri Swapna; Dr Ruchita Sujai Chowdhary; Dr. Mohit Kumar Tiwari; Lalita Kumari; Lebaka Lakshmi Reddy; Dr. Amol Ramrao Dhakne; N. Lavanya; S. Peerani; and Dr. J. Azad Mohamed on May 30, 2026, for Artificial Intelligence Framework For Personalized English Reading And Writing Skill Optimization Using Adaptive Learning Techniques.

Inventors include T Sunandha Tulasi; Shalini R; Maganti Sushanth Babu; Siddunuri Swapna; Dr Ruchita Sujai Chowdhary; Dr. Mohit Kumar Tiwari; Lalita Kumari; Lebaka Lakshmi Reddy; Dr. Amol Ramrao Dhakne; N. Lavanya; S. Peerani; and Dr. J. Azad Mohamed.

The application for the patent was published on June 19, 2026, under issue no. 25/2026.

Abstract: Artificial Intelligence Framework for Personalized English Reading and Writing Skill Optimization Using Adaptive Learning Techniques is the proposed invention. The proposed invention focuses on proposes an artificial intelligence framework for personalized English reading and writing skill optimization using a Transformer- based adaptive learning model. The system analyzes learner interaction data including reading comprehension, vocabulary usage, grammar accuracy, sentence formation, and writing coherence to provide intelligent personalized instruction. The Transformer architecture utilizes multi-head self-attention mechanisms to understand contextual linguistic relationships and generate adaptive learning recommendations. The framework dynamically adjusts reading materials, writing tasks, grammar exercises, and vocabulary activities according to learner proficiency and performance progression. Automated writing evaluation and real-time feedback generation enhance language learning effectiveness and learner engagement. The proposed system improves English language proficiency, reduces instructional limitations of traditional learning methods, and supports intelligent adaptive education through advanced artificial intelligence and natural language processing techniques.

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