ESP32-Based AI Embedded System for Personalized Education

Petrovych, Serhii; Zhou, Stefan-Daniel

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Proceedings of International Conference on Applied Innovation in IT · 2026/04/22 · Vol. 14 · Issue 2 · pp. 311–318

ESP32-Based AI Embedded System for Personalized Education

Serhii Petrovych, Stefan-Daniel Horvath and Chunfang Zhou

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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.

Keywords

Embedded Systems AI Tutor Voice Interface Personalised Learning ESP32 Large Language Models (Llms) Edge Computing Speech Recognition Text to Speech STEM Education.

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