1. Title Page
Project Title: AI-Powered Mental
Health Support Chatbot
Team
Members:
a)
b)
c)
d)
Course:
Software Engineering Lab
Date:
January, 2025
2. Abstract/Executive
Summary
This
project proposes the development of an AI-powered chatbot to provide mental
health support. The system will use natural language processing (NLP) to
understand users’ concerns and provide empathetic, research-backed responses.
It includes a mood tracker, daily affirmations, and recommendations for
professional resources if needed. This chatbot aims to make mental health
support accessible, private, and stigma-free.
3. Introduction
Background:
Mental
health is a growing concern globally, with limited access to affordable and
timely support. Many individuals feel reluctant to seek professional help due to
stigma or lack of resources.
Relevance:
Technology
can bridge the gap by providing an accessible, private, and always-available
solution to users who need support.
Target
Audience:
·
Students
and young professionals
·
Individuals
with mild to moderate mental health concerns
4. Problem Statement
What
is the problem?
Mental
health support is often expensive, inaccessible, or stigmatized, leaving many
without the help they need.
Why
does it matter?
Unaddressed
mental health issues can lead to severe consequences, such as decreased
productivity, strained relationships, and poor quality of life.
Who
is affected?
Individuals
facing mental health challenges but lacking resources or motivation to seek
traditional help.
5. Objectives and
Scope
Objectives:
Develop
a chatbot that can respond empathetically to users’ mental health queries.
Incorporate
a mood-tracking feature to monitor user emotions over time.
Provide
scientifically-backed coping strategies and resources.
Scope:
Included:
Chatbot interface, mood tracker, data encryption for user privacy.
Excluded:
Professional diagnosis or therapy sessions.
6.
Methodology/Approach
Technology
Stack:
·
Frontend:
React.js
·
Backend:
Python (Flask)
·
AI/NLP:
OpenAI GPT API or Hugging Face Transformers
·
Database:
Firebase (real-time database)
·
Deployment:
Google Cloud
Development
Steps:
·
Requirement
Analysis: Collect requirements through research and stakeholder interviews.
·
Design:
Create UI mockups and chatbot architecture.
Development:
·
Implement
chatbot logic using NLP.
·
Develop
frontend for user interaction.
·
Integrate
mood tracker and database.
Testing:
Perform usability and functional testing with a focus group.
Deployment:
Launch on both web and mobile platforms.
7. Timeline
|
Phase |
Task |
Duration |
|
Requirement Analysis |
Research and requirement gathering |
Week 1, 2 |
|
Design |
UI design and architecture
planning |
Week 3, 4 |
|
Development |
AI/NLP chatbot logic |
Weeks 5–10 |
|
Testing |
Functional and usability testing |
Weeks 11–12 |
|
Deployment |
Launch and final documentation |
Week 13 |
8. Resources
·
Development
laptops
·
Internet
connection
·
Access
to mental health datasets for training the chatbot
9. Risk Management
Potential
Risks:
·
Inaccurate
chatbot responses due to limited training data.
·
Data
privacy concerns if user data is not securely handled.
·
Misuse
of the chatbot for purposes other than intended.
Mitigation
Strategies:
·
Use
pre-trained, well-documented NLP models.
·
Encrypt
all user data and comply with GDPR standards.
·
Include
disclaimers about the chatbot’s limitations.
10. Conclusion
The
AI-Powered Mental Health Support Chatbot aims to address the growing need for
accessible mental health solutions. Its innovative use of NLP, mood tracking,
and resource recommendations provides a private and convenient support system.
This project demonstrates how technology can positively impact society by
improving mental health accessibility.
No comments:
Post a Comment