A recent study, Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, “explores the neural and behavioral consequences of LLM-assisted essay writing.”

The general conclusion is not surprising. It appears that using ChatGPT to do the heavy lifting for researching and writing essays resulted in demonstrably less cognitive engagement and intellectual gains.

“While LLMs offer immediate convenience, our findings highlight potential cognitive costs” raising “concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.”

Where does AI fit in to scholarly pursuits and what is its utility?

I’ve recently been asked to help develop the curriculum for a 6000-level course on applications of Artificial Intelligence in Computational Biology, a course that I will be teaching in the Spring of 2026 at the University of Alabama in Huntsville.

Initially, I was simply thinking this would end up being a course to examine current, emerging and potential applications of AI in education and research, broadly related to aspects of systems biology and bioinformatics. But in light of the increasingly rapid development of AI technologies and their applications - and the impacts we are seeing as evidenced by the referenced study - I’m trying to be appreciative of the value and impacts that the current and future technologies will have on education and research; I want to ensure that the course provides real and durable value to the students who give their money, time and attention to it.

Two core high level objectives of academic institutions?

1. Education - The communication of our understanding of the universe and humanity's relationship to it, and how we have come to construct the models that provide the vehicle for communicating that understanding.

2. Research - Advancing humanity's understanding of the natural world and human nature as well as the creation, implementation and understanding of artificial constructs and systems that change humanity's relationship to the natural and constructed world. Roughly speaking, the understanding bits fall under the arts and sciences and the modifying humanity’s relationship aspects (technology) fall under engineering and medicine.

This is, by no means, a standard way to categorize these academic domains. It is really just an ontological framework that makes sense to me (at the moment anyway) and gives me a way to think about the utility of the various aspects of the associated academic devices.

It is within this loose and likely mutating framework that I will be capturing my ideas about how to best apply Artificial Intelligence technologies in various academic pursuits.

More to come soon in: ‘Artificial Intelligence in Academia - Part 2 of ...’