ESP32-Based AI Embedded System for Personalized Education

Abstract

This paper presents the design, implementation, and pedagogical evaluation of the ESP32 AI Tutor – a low-cost, voice-controlled embedded system that serves as an interface to cloud-based large language models for personalised education. Using an ESP32-S3 microcontroller with I2S digital microphone and amplifier, the system captures audio, streams it to AssemblyAI for speech recognition, sends the transcribed text to Groq LLM, and synthesises the response via Google TTS. Four design principles for educational embedded systems are formulated: hardware autonomy, sensory integration, network resilience, and low-latency turn-taking. The open-source implementation is validated through two realistic scenarios – individual adaptive tutoring and group work in a STEM laboratory – demonstrating that the device provides Socratic questioning, adaptive difficulty adjustment, and real-time visual feedback without requiring a PC or smartphone. Experimental results show a total response latency of 0.5–1 s, satisfying natural conversation requirements. The reproducible blueprint and complete code are publicly available on GitHub.