Minimum Viable Product for Mental Wellbeing through Intelligent Kindness Platform

Develop a React Native mobile application that demonstrates workplace wellbeing improvements through basic machine learning recommendations. Students will create a cross-platform mobile app that collects daily wellbeing check-ins, implements a simple recommendation algorithm for personalized workplace interventions, and displays progress tracking. This social venture project focuses on core mobile development skills while introducing students to ML concepts, user experience design for wellbeing applications, and real-world startup development processes. The project emphasizes practical application of classroom knowledge in mobile development, basic data science, and user-centered design while contributing to meaningful social impact in workplace mental health.

MVP Kindness for Wellbeing platform Do Me A Favour Buddy

Develop and implement the "Do Me A Favor Buddy" (DMAFB) platform - an AI-driven wellbeing application that personalises acts of kindness recommendations based on psychological profiling, life events, and user feedback. The platform uses validated psychological assessment tools (Big Five personality traits, self-determination theory) to suggest contextually appropriate kindness activities for both workplace and personal settings, with sophisticated feedback loops and outcome tracking. Students will gain hands-on experience in: Full-stack web/mobile development using modern frameworks Machine learning for personality-based recommendation systems Database design for complex user profiling and longitudinal data tracking API development for survey integration and real-time feedback systems UX/UI design for engaging, psychology-informed user interfaces Data analytics for wellbeing outcome measurement and visualization Agile development practices with real-world psychological research applications Ethics in AI particularly around sensitive personal data and psychological profiling

Proof of Concept + Minimum Viable Product for Mental Wellness Through Guided Kindness Acts

Project Overview A two-stage development of an AI-driven mental wellness intervention, beginning with a WhatsApp chatbot proof-of-concept that will inform the development of a comprehensive business solution. Stage 1: Chatbot Proof of Concept Core Objectives Develop and validate a WhatsApp-based AI chatbot that can: Track and analyze daily user moods Provide personalized kindness suggestions Maintain user engagement through automated check-ins Generate training data for Stage 2 ML models 2.Conduct 8-week validation experiment: Recruit 200-300 participants Implement pre/post well-being assessments Track daily mood patterns Measure completion of suggested kindness acts Analyze effectiveness of different kindness suggestions Stage 2: Business MVP Development Core Objectives Using insights and data from Stage 1 to develop: Enhanced ML-driven system with: Sophisticated need satisfaction analysis Advanced personalization engine Business-specific recommendation algorithms 2.Business-specific features: Employee version functionality Manager dashboard and analytics Team well-being tracking Integration capabilities

Optimising User Experience for DMAFB's MVP

Do Me A Favour Buddy (DMAFB) aims to enhance mental wellbeing through personalised acts of kindness. The current Minimum Viable Product (MVP) needs an optimised user interface (UI) and user experience (UX) to better engage users, ensure seamless navigation, and support the app’s mission. The goal is to create an intuitive, visually appealing, and accessible platform that encourages users to integrate kindness into their daily lives. This project will involve analyzing the existing UI/UX, identifying areas for improvement, and implementing design changes that drive positive mental health outcomes and foster a supportive community. Key tasks include: - Conducting user research to understand pain points and preferences. - Redesigning the UI to be more visually appealing and intuitive. - Enhancing UX to ensure seamless navigation and user engagement. - Testing the new design with a sample user group and iterating based on feedback.