What is your project about? Why did you decide to make it? How does it work? Show us with images and videos!

Smart Study AI Platform is an intelligent study assistant that combines an Arduino UNO Q, physical sensors, a web-based dashboard, and AI to create a more interactive and responsive study environment.

I decided to build this project because, as a student, I know how difficult it can be to stay focused during long study sessions. Sometimes we get distracted, become inactive for too long, or continue studying in an uncomfortable environment without even noticing it. I wanted to create something that would not just tell me to "focus, " but could actually observe the study environment, understand what is happening, and respond to it.

In practice, Smart Study Space Monitor Assistant works best when you follow a step-by-step study validation workflow and keep a simple checklist for wiring, power stability, and expected output behavior. This makes debugging faster and creates a practical system troubleshooting path for repeatable results.

At the center of the project is an Arduino UNO Q connected to a PIR motion sensor, LDR light sensor, and buzzer . The PIR sensor helps detect activity and inactivity, while the LDR monitors the surrounding light level. The buzzer provides physical feedback when important events occur.

For the final version, I moved away from the original OLED display and physical push buttons and built the main interface using Arduino App Lab's WebUI . This gives the platform a much more flexible dashboard where I can control and monitor the entire system from a browser.

The system works through several connected layers.

The Arduino UNO Q firmware handles the physical hardware, sensors, focus-session state, timers, recommendations, and buzzer feedback. It continuously monitors the study environment and keeps track of the user's activity.

The Arduino communicates this information through RouterBridge/RPClite with the Linux-side application running on the UNO Q. The App Lab application then exposes the information to the WebUI, creating the connection between the physical device and the dashboard.

A reliable implementation also benefits from modular structure: separate input handling, processing logic, and output control so each part can be tested independently. That pattern supports low-noise arduino tuning, clearer physical calibration decisions, and safer iteration when features evolve.

From the WebUI, the user can:

• Start, pause, resume, and stop a focus session
• Start a study break
• Monitor motion activity
• Monitor the surrounding light level
• View session and idle time
• Receive study recommendations
• See the current AI status
• Receive visual and audible feedback

The AI recommendation system is what makes the project more than just a sensor monitor. The platform can use the App Lab LLM Brick to generate contextual study recommendations based on information such as the current session state, activity, light level, and idle time.

I also designed the AI as an enhancement rather than something the entire system depends on. If the AI becomes unavailable or an inference fails, the platform can fall back to its built-in rule-based recommendation system. This allows the core study assistant to continue functioning even without AI.

The buzzer also plays an important role in making the system physical rather than purely software-based. Different actions have different audio cues, while prolonged inactivity can trigger an idle warning that continues until the system detects motion again.

The final architecture can be summarized as:

For long-term maintainability, document baseline measurements such as response time, stability under transitions, and recovery after temporary faults. Using this measurement-driven webui optimization style gives you a scalable project upgrade path without turning the project into a fragile one-off demo.

Arduino UNO Q → Sensors & Firmware → RouterBridge/RPClite → App Lab → AI Recommendation Layer → WebUI Dashboard

What started as a simple idea for a study assistant evolved through multiple development phases into a complete hardware and software platform. I went through the process of redesigning the interface, migrating from the original OLED-based approach to WebUI, integrating the Arduino UNO Q and App Lab communication layer, implementing the buzzer feedback system, adding AI recommendations, hardening the software, and preparing the project for release.

I have also tested the complete system on the actual hardware, including the PIR sensor, LDR, focus-session and timer logic, WebUI controls, Bridge communication, buzzer and idle-warning behavior, and AI recommendations with fallback behavior.

This project is ultimately my attempt to build a study assistant that doesn't just sit on a screen—it can actually sense, respond, and interact with the physical environment around the student.

The images and videos below show the actual hardware, the WebUI dashboard, the AI recommendation system, and the different interactions and behaviors of the completed platform.