HelpMe: early detection of university students' mental health issues using a chatbot-integrated dashboard / Nadia Abdul Wahab ... [et al.]

Nadia Abdul Wahab, Nadia Abdul Wahab and Ahmad Asyraf Zainudin, Ahmad Asyraf Zainudin and Norfiza Ibrahim, Norfiza Ibrahim and Aznoora Osman, Aznoora Osman and Abdul Hapes Mohammed, Abdul Hapes Mohammed (2025) HelpMe: early detection of university students' mental health issues using a chatbot-integrated dashboard / Nadia Abdul Wahab ... [et al.]. Journal of Computing Research and Innovation (JCRINN), 10 (1): 18. pp. 204-217. ISSN 2600-8793

Abstract

University students face increasing mental health challenges due to academic, social, and financial pressures, yet a shortage of mental health professionals limits early intervention. To bridge this gap, HelpMe—a web-based system with an interactive dashboard and chatbot—was developed for early mental health detection. The system provides a private space for students to monitor their well-being, with the chatbot guiding users through mental health screenings and offering conversational support, while the dashboard visualizes data for tracking emotional states over time. Developed using the Design Science Research Methodology (DSRM), HelpMe follows a structured process of problem identification, system design, and evaluation. The dashboard prioritizes simplicity and engagement, utilizing Power BI for data visualization, while the chatbot ensures a user-friendly mental health screening experience. User Experience Testing (UXT) with 30 university students assessed the system across six key scales, including attractiveness, efficiency, and dependability. Feedback was largely positive, especially regarding simplicity and visual appeal, though challenges were noted in chatbot responsiveness and dashboard efficiency, with occasional delays. This study highlights HelpMe’s potential as an accessible mental health support tool and identifies areas for improvement. Future enhancements will focus on refining chatbot interactions and optimizing real-time dashboard functionality to better support student well-being

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Nadia Abdul Wahab, Nadia Abdul Wahab
UNSPECIFIED
Ahmad Asyraf Zainudin, Ahmad Asyraf Zainudin
UNSPECIFIED
Norfiza Ibrahim, Norfiza Ibrahim
UNSPECIFIED
Aznoora Osman, Aznoora Osman
UNSPECIFIED
Abdul Hapes Mohammed, Abdul Hapes Mohammed
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Algorithms
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus
Journal or Publication Title: Journal of Computing Research and Innovation (JCRINN)
UiTM Journal Collections: UiTM Journals > Journal of Computing Research and Innovation (JCRINN)
ISSN: 2600-8793
Volume: 10
Number: 1
Page Range: pp. 204-217
Keywords: mental health screening, chatbot, chatbot-integration, early detection
Date: 2025
URI: https://ir.uitm.edu.my/id/eprint/114307
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