This paper reports a recent iteration of investigation in the science of learning, arising from a trajectory of work by the authors dating from 2021 at the intersection of neuroergonomics and data science. The work applies a frame of making and citizen science to the design of learning environments in which students seek to understand their own physiological responses as they participate in activities of learning in contexts authentic to themselves, as opposed to lab-based studies. In the present study, seven high school students were invited to learn about various topics through the reading of curated texts, as well as through interactions arising from prompts to a Large Language Model (LLM). During the activity, the participants wore non-intrusive sensors of electrodermal activity (EDA), as proxy measures of arousal and engagement. TVSymp, calculated using spectral powers of the EDA signal, has been found to correlate highly to orthostatic, cognitive, and physical stress. However, the findings from post-learning quiz results and EDA features do not suggest there to be any significant differences in effectiveness between learning from LLMs and curated texts. The present study serves as a small part of the body of literature related to AI in education to inform policy and to suggest ways forward as school leaders and teachers seek to navigate this evolving landscape.