###### Abstract  
This article introduces a voice-controlled robotic system for early education, enabling children as young as four to interact with robots using natural voice commands. Recognizing the challenges posed by programming languages and robot theory for young learners, this study leverages recent advancements in artificial intelligence, such as large language models, to make robots more intelligent and easier to use. This innovative approach fosters a natural and intuitive interaction between the child and the robot, effectively removing barriers to access and expanding the educational possibilities of robotics in the classroom. In this context, a software pipeline is proposed that translates voice commands into robot actions. Each component is tested using different deep learning models and cloud services to determine their suitability, with the best ones being selected. Finally, the chosen setup is validated through an integration test involving children aged 4 to 6 years. Preliminary results demonstrate the system's capability to accurately recognize and execute voice commands, highlighting its potential as a valuable educational tool for early education.  
c robotics; education; human-robot interaction; artificial intelligence Article  
Vol. 2024 14 2048 2  
and practical skills accessible to young learners, even four-year-olds. With recent advancements in artificial intelligence (e.g., [5; 6]), computer vision (e.g., [7; 8]), and robotics (e.g., [9; 10]), there lies an opportunity to innovate the methods of interaction between children and educational robots [11]. Evidence from recent literature indicates that even preschoolers can develop an understanding of complex scientific and mathematical concepts when provided with intuitive and engaging tools [12; 13].  
Addressing this opportunity, our research introduces a voice-controlled robotic system designed for early education settings. The system integrates advanced technologies, including speech-to-text (STT), large language models (LLMs), and object detection, enabling it to understand and respond to verbal commands from children as young as four. This capability underscores the potential for incorporating sophisticated robotics into early childhood educational environments. Through this implementation, we demonstrate that collaborative robotics, typically reserved for industrial settings (e.g., [14; 15]), are equally effective and engaging in early education, fostering a comprehensive approach that encourages young learners to explore and understand complex concepts across various knowledge domain.  
The primary contributions of this work are as follows:  
* We present a proof of concept for a robotic system responsive to children's verbal instructions. This system not only advances the field of educational robotics with its novel capabilities but also serves as a foundational platform for further empirical exploration within the domain.
* Our comprehensive evaluation of critical technologies, including speech-to-text, large language models, and object detection models, to process and interpret children's instructions, highlights these technologies' potential to enhance educational experiences and pinpoints challenges for future research.
* To the best of our knowledge, this research represents the first attempt to utilize collaborative robots, similar to those in industrial settings, in both suitable and beneficial ways for early childhood education. This endeavor marks a significant step forward in educational robotics and opens new opportunities for development within educational contexts.