Flutter / Dart Frontend
Built the user-facing module interactions in the same mobile technology used by the wider application.
← Back to Projects
CASE STUDY · BACHELOR'S FINAL YEAR PROJECT
AI · GAMIFICATION · WELLBEING
AI-Driven Gamification for Personalized Wellbeing Monitoring
My Final Year Project module within a larger collaborative university mental-health application, focused on making wellbeing interactions more engaging, personalized and trackable.
Flutter / DartFastAPIPythonPostgreSQL
REST APIsAI / ML Integration
PROJECT CONTEXT
A focused module inside a larger collaborative system.
The complete mental-health application was developed collaboratively by multiple student developers, with each student responsible for a specific feature or module. My FYP focused on AI-driven gamification and personalized wellbeing monitoring, designed to integrate into that larger application rather than operate as a standalone product.
THE PROBLEM
Wellbeing tools can become passive if users have no reason to keep engaging.
My module explored how gamification, progress visibility and AI-assisted personalization could make wellbeing interactions more engaging while still keeping the system focused on practical monitoring and user experience.
CORE MODULE FEATURES
Designed around interaction, motivation and personalization.
Mood-Based InteractionUses mood-related input as part of the user's ongoing wellbeing interaction.
Wellbeing ActivitiesSupports activity-based engagement rather than passive information display.
GamificationIntroduces rewards and progress mechanisms to encourage continued engagement.
Progress TrackingProvides visibility into user activity and progression over time.
Personalized FeedbackUses AI-assisted logic to support more relevant responses and recommendations.
App IntegrationDesigned to work within a larger shared university mental-health application.
USER FLOW
A simple interaction loop.
STEP 01User InteractionUser engages with mood and wellbeing features.
STEP 02Activity & ProgressThe module tracks activity and progression.
STEP 03Gamified ResponseRewards and progress cues reinforce participation.
STEP 04Personalized FeedbackAI-assisted logic supports relevant feedback.
TECHNICAL APPROACH
Mobile UI, API services and relational data working together.
FastAPI / Python Services
Used backend services to support application logic and communication between the mobile client and supporting functionality.
PostgreSQL
Worked with relational data structures for user-related and module-related information.
REST API Integration
Connected module functionality to backend services and shared application data through APIs.
DESIGN DECISIONS
Keeping the feature useful, not just gamified.
01
Gamification supports the wellbeing goal
Rewards and progress are used to support engagement rather than turning the experience into a game for its own sake.
02
Personalization is tied to user interaction
The module uses user-related context and activity to support more relevant feedback instead of generic responses.
03
Built for integration
The module was designed around the larger application's architecture, shared services and collaborative development context.
04
Evaluation matters
The project included usability-oriented evaluation and User Acceptance Testing concepts to assess whether the module was understandable and usable by intended users.
WHAT THIS PROJECT DEMONSTRATES
Applied AI inside a real application context.
AI Product Thinking
Using AI as part of a broader product workflow rather than treating the model itself as the product.
Mobile + Backend Integration
Connecting Flutter interfaces with FastAPI services, REST APIs and relational data.
Collaborative Architecture
Designing an individual feature so it can coexist with modules developed by other team members.
User-Centred Evaluation
Considering usability, engagement and user acceptance alongside implementation.
PORTFOLIO NOTE
Project evidence will be shown selectively.
The portfolio will include architecture material, safe interface screenshots and project documentation where appropriate.
It will avoid exposing sensitive mental-health test data or information that belongs to other student contributors.