Classroom behavior analysis is a key component of multimodal learning analytics, which has advanced alongside the digitalization of education, artificial intelligence, and hardware. Several studies have proposed systems to enhance learning outcomes in both online and face-to-face environments. However, hardware solutions to address critical deployment issues remain limited, particularly during face-to-face lessons. These issues include high bandwidth consumption, latency, computing demand, data security, privacy, and anonymity. This work presents a hybrid edge computing model for analyzing student behavior and indoor environmental variables during in-person lessons using AI-powered systems. Our model addresses network, computing resources, and data-related challenges. Tested in three case studies with different classroom and lesson configurations, the results demonstrate the model’s capability to process HD video streams of over 20 students per frame with four deep neural networks at five frames per second, reducing bandwidth consumption from 0.85 Mbps to 0.1 Kbps, conserving computing resources, and ensuring data privacy. Additionally, the model supports indoor environmental variable acquisition, real-time dashboards, and human-computer interaction on the same node device.