MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202621077422 A) filed by Manoj Shankar Mishra; Dr. Sarika Tiwari; Dr V G Sadh; Dr. Sandeep Singh; Dr. Juhi Kamakoty; Dr. Nempal Singh; Dr. Gyanendra Chaturvedi; Pooja Rajput; S K Dixit; and Dr. Seeta Jaiswar on June 23, 2026, for System And Method For Adaptive Cognitive-Linguistic Prompt Engineering And Dynamic English Proficiency Enhancement In Generative Artificial Intelligence Learning Environments.
Inventors include Manoj Shankar Mishra; Dr. Sarika Tiwari; Dr V G Sadh; Dr. Sandeep Singh; Dr. Juhi Kamakoty; Dr. Nempal Singh; Dr. Gyanendra Chaturvedi; Pooja Rajput; S K Dixit; and Dr. Seeta Jaiswar.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: ABSTRACT System and Method for Adaptive Cognitive-Linguistic Prompt Engineering and Dynamic English Proficiency Enhancement in Generative Artificial Intelligence Learning Environments A system and method for adaptive cognitive-linguistic prompt engineering and dynamic English language proficiency enhancement in generative artificial intelligence learning environments are disclosed. The system takes data of learner interaction and request of prompt generation, and generates a digital language twin that represents a learner's linguistic proficiency, communication behavior, cognitive characteristics, and prompt engineering capabilities. An instant DNA extraction engine examines semantic, contextual, instructional and reasoning features related to prompts generated by learners. The semantic divergence analysis module detects the gaps between the learner’s intention and the prompt expression. The communication intent prediction module identifies the underlying interaction objectives. A preliminary simulation engine will forecast the quality of the response and the interaction before the prompt is executed. A hallucination risk assessment module (HRAM) evaluates the reliability of the output and generates risk indicators. An adaptive prompt reconstruction module generates optimized prompt variations, and a competency reinforcement module offers personalized learning interventions. A prompt evolution knowledge graph for preserving a learner progress and communication intelligence. The revealed system enhances communication accuracy, promptness, response dependability, language competence, and human- artificial intelligence interaction performance.
Disclaimer: Curated by HT Syndication.
