An AI-Powered Culturally Aware Chatbot for Stress Detection and Wellness Support among Pakistani University Students Using NLP and Machine Learning

arXiv cs.AI Papers

Summary

This paper presents an AI-powered, culturally aware chatbot for stress detection and wellness support among Pakistani university students, using Random Forest for classification with 89.09% accuracy and integrating an open-source LLM for conversational support.

arXiv:2609.11199v1 Announce Type: new 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.
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# An AI-Powered Culturally Aware Chatbot for Stress Detection and Wellness Support among Pakistani University Students Using NLP and Machine Learning
Source: [https://arxiv.org/abs/2609.11199](https://arxiv.org/abs/2609.11199)
[View PDF](https://arxiv.org/pdf/2609.11199)

> 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\.

## Submission history

From: Muhammad Fahad Bashir \[[view email](https://arxiv.org/show-email/5ce04272/2609.11199)\] **\[v1\]**Thu, 10 Sep 2026 08:08:42 UTC \(655 KB\)

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