Summary
Pakistani university students face many unique pressures that affect their mental health. The authors created a chatbot that uses artificial intelligence to spot signs of stress from student responses and then offers wellness advice. This chatbot understands cultural details and communicates in English, Urdu, and Roman Urdu to better help these students. The system uses a machine learning model that achieved nearly 90% accuracy in identifying stress levels. One key stress factor found was the relationship between teachers and students, showing the importance of cultural context in mental health tools.
What this means in practice
- •For mental health app developers: Integrate culturally aware AI stress detection and wellness conversations tailored for Pakistani university students in multiple languages.$Commercial implications: Enables development of mental health apps tailored for Pakistani students using AI and multilingual chatbots, a niche not addressed by existing Western-focused tools.
- •For university counseling centers: Use AI-driven chatbot outputs to identify and support students experiencing varying stress levels with context-sensitive advice.
Abstract
With the existing digital mental health tools specifically developed for Western settings, Pakistani students are exposed to a uniquely compounded stress situation in their university that includes academic, financial, familial, and relational stressors, which have become a serious concern for academic and psychological development of students in Pakistani universities. This paper introduces a new, AI-driven and culturally sensitive stress detection and wellness support system that is tailored to the context of Pakistani university students. The system is based on a machine learning model called Random Forest which is trained using a validated student stress data set of 1100 responses on 20 features from psychological, physiological, academic, environmental and social aspects, with an accuracy of 89.09% and a macro F1-score of 0.89, in three stress severity levels. The classification outputs are passed on to an open-source large language model through OpenRouter API, where an appropriately crafted system prompt, culturally aware, gives the model a conversation about wellness, in English, Urdu and Roman Urdu. The second most predictive stress factor in this population identified by feature importance analysis was teacher-student relationship, which is a culturally important stress factor highlighting the need for region-aware mental health systems. Future research will involve primary data collection from students at various academic levels of Pakistani Universities with the validated DASS-21 instrument focusing on the students who are moving from FSc to undergraduate studies, which is a time of being psychologically vulnerable which is under-researched.