MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082153 A) filed by Cmr Institute Of Technology on July 03, 2026, for System And Method For Ai-Based Student Dropout Prediction And Automated Counseling Intervention.
Inventors include Varun Anjaneya Prasad Avisinedi; Anjanee Kumar Avula; Nikhil Bikkanoori; Praneeth Reddy Buchaiahgari; Prasanna Sree Tanneru; and Vasavi Suniganti.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: The present invention discloses an AI-based drop-out prediction and counseling system configured to address student attrition in educational institutions through integrated data fusion, predictive risk scoring and automated, targeted intervention. The system comprises a data fusion module that consolidates heterogeneous student data, including academic scores, attendance records, fee payment status and examination backlogs, from disparate institutional sources into a single unified dashboard accessible to mentors and administrators. A risk prediction module applies rule-based logic and a Random Forest machine learning classifier to the consolidated data to compute a composite, color-coded risk score for each student, enabling early identification of at-risk individuals well in advance of visible crisis. Upon detection of elevated risk, an automated intervention and notification module generates tailored academic, emotional and financial counseling recommendations and dispatches transparent alerts to mentors, parents and guardians through an asynchronous digital notification channel. A student-facing engagement panel further provides an attendance tracker, backlog resolution guidance and curated study resources, fostering collaborative academic recovery. Implemented using a scalable, cloud-deployable architecture comprising a web-based dashboard, backend server, document-oriented database and asynchronous task queue, the invention transforms fragmented, reactive student monitoring into a proactive, transparent and actionable retention framework, thereby improving student outcomes, reducing mentor workload and strengthening institutional and parental engagement.
Disclaimer: Curated by HT Syndication.
