MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641100478 A) filed by Christ University on August 20, 2026, for An Ai-Based Multimodal Academic Burnout Prediction And Personalized Support System Using Premenstrual Syndrome Indicators Acadmic Resilience And Learnd Helplessness Patterns.
Inventors include Shruthi R; and Jacqueline Kareem.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention discloses an AI-based multimodal academic burnout prediction and personalized support system using premenstrual syndrome indicators, academic resilience, and learned helplessness patterns. The system is also equipped to consider adolescent female studentspecific conditions, including premenstrual syndrome indicators, academic resilience indicators, learned helplessness indicators, academic workload data, academic engagement data, and academic burnout-related indicators. A feature extraction module classifies the received inputs into multiple dimensions, including physiological symptoms, psychological symptoms, behavioral symptoms, perseverance, reflective coping, adaptive help-seeking, cognitive decline, emotional decline, motivational decline, exhaustion, cynicism, and inadequacy. A burnout risk computation engine processes the extracted features using artificial intelligence models, dynamic weighting, cycle-aware normalization, resilience- buffering analysis, and learned helplessness progression analysis to generate a personalized academic burnout risk score. Based on the computed score, an intervention recommendation module generates personalized academic support actions, including mentor alerts, counseling referral suggestions, workload management prompts, resilience-building activities, adaptive help-seeking prompts, and follow-up monitoring schedules. A feedback learning module updates the risk profile based on student responses, intervention completion, and subsequent well- being inputs. The invention enables early prediction of the academic burnout risk and the delivery of non- diagnostic academic support interventions.
Disclaimer: Curated by HT Syndication.
