The underlying potential of NLP for microcontroller programming education

The trend for an increasingly ubiquitous and cyber-physical world has been leveraging the use and importance of microcontrollers ($\mu$C) to unprecedented levels. Therefore, microcontroller programming ($\mu$CP) becomes a paramount skill for electrical and computer engineering students. However, $\mu$CP poses significant challenges for undergraduate students, given the need to master low-level programming languages and several algorithmic strategies that are not usual in “generic” programming. Moreover, $\mu$CP can be time-consuming and complex even when using high-level languages. This article samples the current state of $\mu$CP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of $\mu$CP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8-bit $\mu$C and project-based learning. While NLP tools emerge as strong candidates as students' $\mu$C companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for $\mu$CP education, building on both hands-on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real-world tasks that the $\mu$C is designed to handle, rather than on low-level coding. We also analyzed the specific impact of chatGTP in the context of a $\mu$CP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in $\mu$CP courses.