International Journal of Science, Engineering and TechnologyAuthors: Omkar Gawde, Nupun Sawant, Dr. Jasbir Kaur, Assistant Professor Mansi Rajapurkar
Abstract: Organizing institutional educational visits involves a complex set of tasks itinerary coordination, attendance verification, real-time safety oversight, and post-trip documentation that most colleges still handle through paper registers, printed schedules, WhatsApp groups, and phone calls. This paper describes IV Planner, a web-based platform purpose-built for managing educational field visits. The platform brings together AI-assisted itinerary drafting (powered by Google Gemini with JSON schema validation), QR-code attendance, GPS-aware geofencing using the Haversine formula, GPS-tagged photo logging, and Socket.IO-driven emergency alerts under one unified interface. The backend stack comprises React/Vite on the client side, Express/Node.js on the server, MongoDB for persistence, and a structured validation layer that treats every Gemini response as untrusted until it clears required-field checks and server-side range validation. A 30-day field deployment with 52 participants across four institutional visits measured SOS dispatch latency, geofence notification time, QR scan confirmation, API response times, WebSocket propagation delay, battery consumption, and concurrent connection stability. Manual baselines were observed separately and converted to milliseconds purely for operational comparison. The deployment recorded a mean SOS dispatch latency of 310 ± 42 ms, geofence notification latency of 480 ± 65 ms, and QR scan-to-confirm time of 620 ± 80 ms. The system handled 350 simultaneous WebSocket connections without errors and was stress-tested to 500, where a 1.24% error rate appeared. Findings indicate that combining WebSocket event delivery, server-side geofence validation, database-enforced attendance integrity, and structured LLM output validation is feasible for real-world educational visit scenarios, though broader multi-institution studies are still needed before firm generalizations can be drawn.
