578 MindGuard AI: An Intelligent Burnout Prevention Ecosystem for Higher Education

Added: 2026-05-12

[00:00:05]Good day respected judges. I am TS Dr. Dioban and my team member is Dr. Tan Wheeling. We are from University Science Malaysia. Today we present my gut AI and intelligent burnout prevention ecosystem for higher education. Our tagline is protecting minds empowering futures.

[00:00:24]In higher education, we often focus on academic excellence, research output, and student achievement. But behind this success, many students and lecturers are quietly facing burnout, stress, emotional exhaustions, and academic pressure. The problem is that support often comes too late, only after crisis signs have appeared. The current gap is clear. Many university support systems are reactive rather than preventive.

[00:00:49]Counseling is important but it usually respond after serious distress appears.

[00:00:54]Manual monitoring is limited and most academic analytics focus on grades and attendance while emotional well-being is often overlooked. The problem flow usually start with academic pressure.

[00:01:05]This can lead to emotional exhaustions, burnout for performers, dropout risk or mental health crisis. UNESCO reported in 2025 that nearly one in three higher education students report mental health challenges. Universities should not wait until students and lecturers reach crisis point before action is taken.

[00:01:25]This is why we created my gut AI. It is an AI powered burnout prevention ecosystem that detects early emotional and behavioral respect among students and lecturers. It combines AI predictive analytics, educational data, emotional selfch checkins, psychological intervention logic, and an institutional dashboard. This slide shows the system architecture. Ma AI begins with data inputs such as LMS engagement, attendance, assessment, submissions, emotional check-ins, and workload indicators. This data are processed securely and analyzed by the AI intelligence engine. The system then generates early alerts, wellness prompts, reflective exercises, counselor referral and institutional insights.

[00:02:10]Mag AI works through four phases. First, it collects behavioral and emotional signals. Second, it predicts burn out.

[00:02:18]Third, it recommends personalized support such as wellness prompts or referral. Fourth, it provide institutional dashboard insights for preventive decision making. This makes support earlier, more targeted and more practical.

[00:02:31]The system uses data sources already familiar to universities. This includes LMS engagement, attendance patterns, assessment behavior, emotional selfcheck-ins, and workload indicators.

[00:02:43]For example, repeated non-submission, declining participations, or high workload can become early signals for support.

[00:02:50]This is the prototype dashboard. It allows institutions to view high-risisk cases, alerts, emotional check-in patterns, and AI recommendations. The mobile interface allows users to completely daily check-in and receive personalized prompts. My AI does not only detect risk. It turns emotional sickness into timely and actionable support.

[00:03:11]What makes my gut AI different?

[00:03:13]Conventional system are reactive but my gut AI is predictive. Conventional system focus mainly on academic performance but my gut AI focuses on emotional sustainability and academic success together. It also provide personalized well-being intervention instead of generate support. The figure shows the impact ecosystem of my AAI.

[00:03:34]The system creates value for students, lecturers, universities, and society.

[00:03:39]It's not only designed to support individuals, but also to build a healthier and more emotionally intelligent higher education ecosystem.

[00:03:47]For students, it reduces burnout risk and improve engagement. For lecturers, it supports early burnout detection and sustainable productivity. For universities, it strengthen datadriven interventions and healthier campus culture. For society, it contributes to healthier graduates and a sustainable workforce. It also support SG3 and SG4.

[00:04:08]Mega AI has strong commercialization potential. The target market includes universities, colleges, TV institutions, student well-being centers, and educational ministries. It can be commercialized through institutional subscription, LMS integration, dashboard analytics, Asian expansion, and government collaboration. Future development may include an AI well-being chat box, realtime emotional analytics, smart variable integration, personalized resilience coaching, and an institutional emotional sustainability index. This makes my AI scalable, practical, and future ready. To conclude, the future of education is not only about intelligent, technology, and academic achievement. It is also about well-being, prevention, and human sustainability. My that AI protects minds before burnout become a crisis.

[00:04:58]Thank you.