The impact of AI tools like ChatGPT on cognitive load in knowledge work is not yet fully understood in the evolving field of human-AI interaction. This study aims to explore the cognitive load dynamics arising from AI-assisted tasks, revealing their potential to streamline workflows, but also risking cognitive overload, potentially hindering task performance, learning, and enjoyment. Anchored in cognitive load theory, our research proposes an experiment that leverages wearable EEG technology to empirically investigate the cognitive load fluctuations experienced by individuals engaged in AI-assisted programming tasks. By dissecting the interplay between user engagement and AI assistance, this study seeks to uncover effective patterns of AI tool usage that mitigate cognitive overload. Thereby, we aim to contribute to cognitive load theory by detailing the interactive load dynamics inherent in AI-assisted work.