EduCare: AI EdTech Command Center for Personalized Learning - @ Google AiStudio - Live Demo

Project Overview

The EduCare AI Edtech SaaS Dashboard was developed to serve as the central intelligence hub for an adaptive learning platform. Built with React, TypeScript, and Tailwind CSS, the dashboard provides students, educators, and administrators with a 360-degree view of learning progression, behavioral habits, and AI-driven insights, replacing static reports with dynamic, actionable data visualizations.

Technology Stack: React (with TypeScript), Tailwind CSS, D3.js/Recharts, Internal AI Model API Integration 

My Role: Lead Front-End Architect, Data Visualization Specialist, UX/UI Designer

1. The Problem: Generic Learning Paths and Data Overload

In the modern education landscape, educators are drowning in raw data (quiz scores, time spent, assignment completion) without the tools to convert it into personalized learning strategies. The core challenges were:

• Lack of Contextual Insight: Existing systems provided grades but failed to identify the root cause of low performance (e.g., a specific knowledge gap, time management issues, or engagement fatigue).

• Inefficient Teacher Intervention: Teachers spent excessive time manually cross-referencing disparate data points, delaying timely intervention for at-risk students.

• Low Student Self-Efficacy: Students lacked a clear, digestible view of their own progress and next best steps, leading to passive consumption rather than proactive engagement.

The mandate was to leverage AI to distill complex learning data into three clear outputs: performance, predictive risk, and personalized tasks.

2. The Solution: Predictive and Prescriptive Learning Intelligence

The EduCare dashboard was architected as a fast, responsive Single Page Application (SPA) designed for multi-stakeholder use (Student, Teacher, Admin).

• AI-Driven Mastery Scoring: Implemented rich visualizations (e.g., radar charts and sunburst diagrams) to map performance against specific competencies, showcasing not just what a student scored, but their current Mastery Level and identified Knowledge Gaps.

• Study Habit Analysis: Integrated custom charts to visualize behavioral data (time-on-task, study session consistency, content consumption patterns), allowing the AI to flag potential burnout or disengagement early.

• Predictive Risk Flagging: A prominent KPI card uses the AI model's output to assign a "Performance Risk Index," enabling teachers to instantly view and filter students who are statistically likely to fall behind in the next assessment.

• Adaptive Recommendation Engine: The dashboard serves up the AI’s prescriptive recommendations (e.g., "Review Module 3.2 on Supply Chain Fundamentals" or "Schedule 15 minutes of spaced repetition practice"), turning data analysis into an actionable to-do list.

3. The Design Process: The 360-Degree User Perspective

Our design methodology focused on delivering specialized data views for each user group while maintaining a consistent design system (facilitated by Tailwind CSS).

• Persona-Specific Dashboards:

  • Student View: Focused on motivation and clear next steps ("What should I do now?").

  • Teacher View: Focused on class-wide segmentation and intervention ("Who needs help and why?").

  • Admin View: Focused on longitudinal course efficacy and platform usage metrics.

• Data Translation: We employed TypeScript for strict data typing to ensure the mathematical accuracy of all performance metrics. A key design challenge was translating complex statistical scores (like the "Z-Score of Engagement") into simple, intuitive visual elements.

• Mobile-First Responsiveness: Given the high likelihood of student access via tablets and phones, the Tailwind CSS framework was critical in ensuring the dense data visualizations remained clean, legible, and fully responsive across all device breakpoints.

4. The Final Product: A Lift in Engagement and Performance

The EduCare AI Dashboard successfully centralized and personalized the learning experience. The tool provided clarity to students about their learning journey and efficiency to educators, shifting intervention from reactive grading to proactive coaching. Within the pilot program, data showed a:

• 15% Increase in Student Platform Engagement due to clearer, personalized goals.

• 12% Lift in Average Module Completion Rates for at-risk student groups.

• 20% Reduction in Teacher Preparation Time spent analyzing individual performance reports.

AI Turbine Weather Application: Predictive Intelligence for Renewable Energy - @ Google AiStudio - Live Demo

Project Overview

The AI Turbine Weather Application is a responsive, AI-powered dashboard designed for the comprehensive monitoring and analysis of utility-scale renewable energy assets, including wind turbines and solar plants. It integrates real-time data on energy production, localized weather conditions, system performance, and operational metrics. By merging these datasets, the platform provides predictive intelligence to optimize energy output and minimize maintenance costs.

My Role: UX Designer, Data Visualization Specialist, Front-End Developer

1. The Problem: Unpredictability and Inefficiency

Renewable energy production is inherently dependent on highly variable weather conditions, leading to significant challenges in accurate forecasting, grid management, and operational scheduling. Asset managers struggled with:

• Reactive Maintenance: Scheduling maintenance based on historical data or generic timeframes, often leading to unnecessary downtime or, worse, catastrophic failure due to unforeseen weather stress.

• Inaccurate Forecasting: Generic weather data often fails to capture the microclimates of remote plant locations, resulting in poor energy production forecasts and compliance penalties.

• Data Overload: The sheer volume of telemetry data from turbines and solar arrays, combined with meteorological inputs, was overwhelming and difficult to translate into actionable decisions.

The core challenge was to transform vast, complex data into a clear, predictive tool that enables proactive asset management.

2. The Solution: A Unified, Predictive Intelligence Platform

The AI Turbine Weather Application provides a single source of truth for all renewable asset performance data, enhanced by AI-driven predictive modeling.

• Real-Time & Predictive Visualization: The dashboard unifies energy production metrics with hyper-local weather conditions (wind speed, solar irradiance, temperature, precipitation). Crucially, it provides AI-powered forecasts for the next 24-72 hours, enabling predictive resource allocation.

• System Health & Anomaly Detection: The application uses operational metrics to instantly flag anomalies in system performance that correlate with adverse weather events (e.g., turbine vibration at high wind speeds).

• Geospatial Asset Mapping: A dynamic map view displays all assets, color-coded by performance or alert status. Users can instantly see which plants are underperforming due to localized weather issues.

• Responsive Design: Built as a responsive web application, the dashboard ensures energy managers can access critical data and respond to alerts efficiently, whether they are in the office or in the field via a tablet or mobile device.

3. My Design Process: Focusing on Forecast and Alert Hierarchy

The design process prioritized clarity and actionable information, focusing on presenting complex time-series data in a digestible format.

• UX Research: I conducted needs assessments with renewable energy operators, revealing that the wind speed forecast and inverter temperature were the most critical data points for immediate action. This informed the primary layout, placing these key metrics and associated AI forecasts front-and-center.

• Information Architecture: The structure was designed around a "Global Overview" leading to "Asset-Specific Deep Dives." The main dashboard uses a traffic light system (green, yellow, red) for asset health, with clear navigation to detailed performance charts.

• Data Visualization: I used highly effective data visualization techniques, preferring line charts for trends and heatmaps for geographical performance. I ensured a high contrast, professional color palette suitable for long-term monitoring. The key innovation was the integration of weather forecast lines directly over energy production graphs—a visual correlation that immediately validates the AI's predictions and aids user trust.

4. The Final Product: Maximized Output, Minimized Risk

The AI Turbine Weather Application successfully translates raw data and complex AI models into a clean, actionable user experience. It provides asset managers with the foresight necessary to move from reactive decision-making to a proactive, predictive operational strategy. This results in maximized energy output, extended equipment lifespan, and reliable contribution to the power grid. This project showcases proficiency in developing sophisticated, data-heavy applications that deliver tangible financial and operational value in the critical renewable energy sector.

CineAI: A Modern Entertainment Booking Experience - @ Google AiStudio - Live Demo

Project Overview

CineAI is a responsive React application that redefines the entertainment booking experience. Inspired by platforms like BookMyShow, it allows users to browse movies, events, and sports, view detailed information, and simulate a seamless seat selection process. The platform's standout feature is an integrated AI-powered chat assistant that provides personalized recommendations and instant information.

My Role: UX Designer, Front-End Developer, and Product Designer (as a solo project)

1. The Problem: Information Overload & The Static Experience

Traditional entertainment booking websites often present a static, overwhelming grid of content. Users have to sift through endless lists to find something they might be interested in, and the process of finding specific information or recommendations can be clunky and slow. The key challenge was to address this by creating a dynamic, interactive, and intelligent platform that personalizes the booking journey and makes finding the perfect entertainment simple and enjoyable.

2. The Solution: An AI-Powered, User-Centric Platform

The solution was to build a modern web application that combines a beautiful user interface with the power of artificial intelligence. Key features include:

• AI-Powered Chat Assistant: The core of CineAI is a conversational AI assistant. Users can ask questions like, "What are the top-rated action movies playing this weekend?" or "What's a good movie for a family with young kids?" The AI provides instant, relevant, and personalized recommendations, transforming a tedious search into a fluid conversation.

• Seamless Browsing Experience: The platform features a clean, responsive design with clear categories for movies, events, and sports. Users can easily browse through content carousels, and a quick click or tap reveals detailed information about each title, including synopses, cast details, and trailers.

• Interactive Seat Selection: To simulate a real-world booking experience, I created a simulated seat selection module. Users can select their preferred seats on a virtual theater map, providing a preview of the booking process.

• Responsive Design: The application is built to be fully responsive, ensuring a consistent and high-quality experience across all devices, from desktops to mobile phones.

3. My Design Process: Focusing on Intuitive Interaction

My design process was centered on making the AI chat assistant feel like a natural part of the booking experience, not just a gimmick.

• UX Research: I conducted a competitive analysis of entertainment booking platforms and conversational AI interfaces. I paid close attention to the way users ask questions and the types of information they seek, which helped me design a conversational flow that feels intuitive and helpful. I also studied the most effective visual design patterns for presenting a large amount of content in a digestible, engaging way.

• Information Architecture: I designed a simple, flat information architecture that prioritizes quick access to core features. The AI chat is persistent and always available, acting as a shortcut to any information the user needs.

• Prototyping & Visual Design: I began with low-fidelity wireframes to map out the user flow, particularly for the AI chat and the seat selection process. The visual design focuses on a clean, dark theme with vibrant, eye-catching posters. I used React for its powerful component-based architecture, which allowed me to build modular UI elements for the content cards, the chat bubble, and the interactive seating chart, ensuring a scalable and consistent design.

4. The Final Product: A Glimpse into the Future of Booking

The final product is a modern, responsive, and intelligent application that showcases how AI can be seamlessly integrated into a traditional booking process. CineAI is a testament to the idea that technology should simplify and personalize the user experience. It demonstrates a forward-thinking approach to product design and development, proving that AI is not just a feature but a powerful tool for creating truly delightful and efficient digital experiences.

Key Learnings & Outcomes:

• The Power of Conversational AI: The project demonstrated that AI can be a powerful tool for enhancing user experience, transforming a static search into a dynamic, personalized conversation.

• Integration is Key: The seamless integration of the AI chat assistant into the main UI was crucial for its success, making it feel like a natural part of the application rather than an add-on.

• Building for the Future: CineAI is a glimpse into the future of entertainment booking, where technology anticipates user needs and provides a smarter, more efficient, and more enjoyable experience.