DropGuard, Early Dropout Risk Detection with Gemini 3

Title: DropGuard, Early Dropout Risk Detection Using Gemini 3

DropGuard is an early-warning system designed to identify students at high risk of dropping out before academic failure becomes irreversible. The application uses the Gemini 3 Pro model as its core reasoning engine to analyze structured educational data, including academic performance trends, attendance patterns, behavioral indicators, and socio-economic context.

Gemini 3 is used for multi-factor reasoning rather than simple scoring. It evaluates grade trajectories over time, detects risk acceleration (e.g., rapid academic decline), and correlates these patterns with contextual constraints such as transportation, employment, and device access. The model produces a normalized risk score, categorical risk levels, and a human-readable explanation that mirrors how school counselors assess cases in real-world settings.

In addition to risk assessment, Gemini 3 generates targeted, actionable intervention plans tailored to the student’s dominant risk factors. A built-in “What-If” simulation allows users to explore how improvements in attendance or academic performance would alter the predicted risk, demonstrating the system’s potential as a decision-support tool rather than a punitive classifier.

By combining structured data analysis, causal reasoning, and explainable recommendations, DropGuard demonstrates how Gemini 3 can support early, ethical intervention in education systems.

Note: This project is hosted on Google AI Studio and may prompt users to sign in with a Google account to accept platform usage terms. No payment, subscription, or special access is required.

Built With

• gemini3
• google-ai-studio
• react