LLMs for Coding and Robotics Education

TL;DR

This paper explores the use of large language models in robot coding education, demonstrating GPT-4V's superior performance in code tasks but limitations in generating block diagrams, highlighting their potential and current challenges.

Contribution

It evaluates multiple large language models on robot coding tasks, emphasizing GPT-4V's strengths and weaknesses in educational applications.

Findings

GPT-4V outperforms other models in coding tasks

GPT-4V struggles with generating block diagram images

Large language models are promising tools for robot coding education

Abstract

Large language models and multimodal large language models have revolutionized artificial intelligence recently. An increasing number of regions are now embracing these advanced technologies. Within this context, robot coding education is garnering increasing attention. To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification. In this paper, we highlight an important trend in robot coding education. We test several mainstream large language models on both traditional coding tasks and the more challenging task of robot code generation, which includes block diagrams. Our results show that GPT-4V outperforms other models in all of our tests but struggles with generating block diagram images.