AI for Early Detection of Student Burnout
Student
Jeremiah Gladson R
jjaisonjeremiah@gmail.com
Department Of Computer Science
Sri Ramakrishna College of Arts
And Science
Assistant Professor
Dr.N.Mahendiran M.Sc.,M.Phil.,Ph.D
mahendiran@srcas.ac.in
Department Of Computer Science
Sri Ramakrishna College of Arts and Science
Abstract
Today, student burnout is something we see very commonly in colleges and schools. Most students are handling continuous assignments, exams, records, projects, presentations, and sometimes even family responsibilities at the same time. Because of this, they rarely get proper rest. When this kind of pressure keeps building up, it slowly turns into stress and mental tiredness. Some students start losing interest in classes, some feel low without knowing the exact reason, and others find it hard to focus even on simple tasks.
Even though many institutions conduct feedback surveys or provide counselling facilities, these methods are not always enough. The main reason is that students have to openly say they are struggling. But in reality, not everyone feels comfortable sharing their problems. Some may think it is normal stress, while others may hesitate to talk about it. So, in many cases, the early warning signs of burnout are simply ignored.
To reduce this issue, this research proposes an AI-based system that can help in detecting burnout earlier. Instead of waiting until a student’s marks drop or attendance decreases, the system analyses the written content students already provide, such as feedback forms, online discussion messages, or journal-type entries. Using Natural Language Processing and Machine Learning, it checks the emotional tone and repeated stress-related expressions in their writing. From this, the system calculates a burnout risk score for each student.
The idea is not to replace teachers or counsellors. It is only to support them by giving an early indication. If a student seems to be at risk, the institution can step in at the right time and provide proper guidance or counselling. The system also takes care of student data privacy and keeps improving its accuracy as more data is processed. In simple terms, this approach helps create a more caring academic environment where students’ mental health is noticed before the situation becomes serious.
