This advanced Digital Measurement Computer System for Biology offers a comprehensive, modern educational solution by integrating wireless sensors, interactive software, and artificial intelligence. The system includes a wide array of sensors, each featuring a built-in 1.8-inch color LCD for immediate data visualization. These sensors function completely autonomously, eliminating the need for external data loggers or analog-to-digital converters to communicate with computers or tablets.
The included sensor suite features: – pH sensor (0–14 pH, 0.01 resolution)
– Light sensor (0–8000 lux, 1 lux resolution)
– Pressure sensor (0–700 kPa, 0.1 kPa resolution)
– Carbon dioxide sensor (0–50,000 ppm, 1 ppm resolution)
– Humidity sensor (0%–100%, 0.1% resolution)
– Respiration sensor (-10 L/s to 10 L/s, 0.01 L/s resolution)
– ECG sensor (0–5 V, 0.1 mV resolution, 50–200 Hz sampling)
– UV radiation sensor (0–11 UV Index, 200–370 nm detection)
– Ambient temperature sensor (-30°C to +50°C)
– Heart rate sensor (30–200 bpm, 1 bpm resolution)
– Blood pressure sensor (0–375 mm Hg)
– Surface temperature sensor (-25°C to 125°C, 0.1°C resolution)
– Soil moisture sensor (0–100%, 0.1% resolution)
– Skin electrical resistance sensor (0–20 µS)
– Spirometer sensor (0–50 cpm, 0.05 cpm resolution)
– Stethoscope sensor (real-time audio and graphical visualization)
– Dissolved oxygen sensor (0–20 mg/L, 0.1 mg/L resolution)Complementing the hardware is a suite of digital learning materials hosted on an LMS platform. Developed according to the national curriculum, these materials follow an inquiry-based approach and include structured modules, experiment scenarios, step-by-step instructions, video demonstrations, and interactive exercises.
A key feature of the system is the integrated RAG-based AI Agent. This tool operates on a localized knowledge base to select relevant research tasks, explain experimental procedures, and assist in generating reports. It supports inclusive learning by adapting tasks to various proficiency levels and providing recommendations based on the analysis of common student errors.
