MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641095609 A) filed by Saveetha Engineering College on August 07, 2026, for Artificial Intelligence-Based Classroom Literacy Intelligence And Predictive Learning Analytics System.
Inventor includes Dr R. Subhashini.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: Artificial Intelligence (Al) has surfaced as an innovative advancement in education, enabling intelligent, data-informed solutions that augment teacher efficacy and elevate student learning results. Notwithstanding considerable progress in digital education, some classes still depend on conventional assessment techniques that provide restricted understanding of students' literacy growth, learning habits, and prospective academic success. These traditional methods often detect learning challenges only after pupils have encountered significant academic failures, therefore limiting chances for prompt intervention. The suggested system perpetually collects and examines diverse educational data produced throughout classroom learning experiences. This information encompasses reading fluency, pronunciation precision, vocabulary development, reading understanding, writing skill, classroom involvement, attendance, student engagement, evaluation outcomes, behavioural tendencies, and contact with digital educational tools. Through the application of sophisticated Al algorithms to these varied datasets, the system constructs detailed learner profiles that precisely represent each student's reading skills, cognitive development, and learning inclinations. A notable characteristic of the suggested framework is its capacity to provide customized learning trajectories suited to the specific requirements of each learner. The system autonomously suggests personalized reading resources, vocabulary enhancement tasks, pronunciation drills, writing development exercises, comprehension activities, and tailored instructional methods based on realtime analysis of each learner's skill level and learning speed. This tailored method boosts student involvement, fortifies literacy development, and fosters ongoing academic progress while facilitating inclusive education for individuals with varied capabilities and educational histories. Automated notifications inform educators with children in need of prompt academic assistance, facilitating timely instructional measures and alleviating the administrative load linked to human progress tracking. Moreover, explainable AI methodologies guarantee that predicted results are clear and comprehensible, enabling educators to grasp the rationale behind AI-generated suggestions and cultivating confidence in automated educational frameworks. Artificial Intelligence (AI) has surfaced as an innovative advancement in education, enabling intelligent, data-informed solutions that augment teacher efficacy and elevate student learning results. Notwithstanding considerable progress in digital education, some classes still depend on conventional assessment techniques that provide restricted understanding of students' literacy growth, learning habits, and prospective academic success. These traditional methods often detect learning challenges only after pupils have encountered significant academic failures, therefore limiting chances for prompt intervention. The suggested system-perpetually collects and examines diverse educational data produced throughout classroom learning experiences. This information encompasses reading fluency, pronunciation precision, vocabulary development, reading understanding, writing skill, classroom involvement, attendance, student engagement, evaluation outcomes, behavioral tendencies, and contact with digital educational tools. Through the application of sophisticated Al algorithms to these varied datasets, the system constructs detailed learner profiles that precisely represent each student's reading skills, cognitive development, and learning inclinations. Predictive machine learning algorithms identify performance patterns, anticipate future academic success, recognize early literacy difficulties, and assess the probability of learning hazards prior to their substantial impact on educational results.
Disclaimer: Curated by HT Syndication.
