Publication: Explainable AI Powered Mental Health State Capturing Application to Support Students’ Mental Wellness and Academic Stress Mitigation
Type:
Article
Date
2025-07-08
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
SLIIT City UNI
Abstract
Mental health is a state of well-being that enables
individuals to manage stress, work effectively, and contribute
to society. However, reports show that serious mental health
problems among students worldwide are increasing rapidly. A
critical problem is that students often fail to recognize mental
health issues or the sources of their academic stress, leading to
silent suffering that escalates over time. A significant research
gap exists as current assessments methods lack the ability to
identify root causes of academic stress and provide explainable
decisions for clinical use. This significant rise in many students’
mental health issues have indeed opened important discussions
about its underlying causes, consequences, and the need for a
comprehensive support system. Voices are an important part
for identifying emotional expressions, as speech is the most
vital channel of communication, enriched with emotions. The
system analyzes emotional patterns in students' voices using
Natural Language Processing (NLP) techniques to identify
eight emotions and reveal the root causes of their mental health
challenges and academic or non-academic stress. Additionally,
Explainable AI (XAI) techniques are employed to provide a
comprehensive analysis of these patterns, enhancing
understanding and supporting managerial decision-making.
The system achieves 93.46% accuracy using Random Forest
algorithm with reliable confidence levels for clinical
applications. It operates effectively in uncontrolled
environments with language-independent features, ensuring
adaptability across diverse student populations. While
students typically seek support from counselors and healthcare
professionals who base their decisions on clinical experience,
this system offers an additional diagnostic tool to complement
and validate professional evaluations. This research aims to
better understand student mental health issues and contribute
to improved students’ wellness and academic success.
Description
Keywords
speech emotion recognition, explainable AI, mental health, student wellness, machine learning
