It is my belief that AI in its current form has no place in a current academic environment.

For my Senior Independent Study, I am reading the entirety of the published Garfield comic, and I thought it prescient to ask ChatGPT about some of the more minute details of the comic, as a way to test its accuracy. I assumed that, given how thoroughly Garfield has been documented, both in comic form and in discussion branching from the comic, there would be sufficient data for the LLM to make its estimations off of. When I asked “What are the names of Jon Arbuckle’s Neighbors?” GPT gave me a list: Lyman, Ms. Feeney, Mr. and Mrs. Wilson, and Randy. There are a few issues with this list, those being Lyman and the Wilsons. Lyman himself was Jon’s roommate for a time, before his character was slowly phased out. While this is an obvious hallucination, its mistake is still somewhat understandable. The Wilsons present a bigger issue. No such characters exist within the comic, nor, at least as far as I was able to find, within any other form of Garfield media. While a married couple does live next door to Jon, their names being Hubert and Reba, no last name has ever been provided. Curious, I requested the first names of the Wilsons, which ChatGPT responded with :

“I need to correct my previous answer: Mr. and Mrs. Wilson do not appear to have established first names in the Garfield comic strip. I can’t find reliable evidence that either character was given one.

So the answer is simply:

Mr. Wilson: first name unknown/not established

Mrs. Wilson: first name unknown/not established

My earlier mention of the Wilsons as Jon’s recurring neighbors was also likely mistaken. Thanks for prompting me to check it”

I further requested that the LLM “Provide the dates for the first appearances of Mr. and Mrs. Wilson within the Garfield comic strip,” to which ChatGPT refused to dignify my request with a response. Further testing revealed that, in fact, ChatGPT was now refusing to answer any questions.

In this sense, I argue that, while some use cases for an LLM are better than others, there is fundamentally no place for LLM usage in academia not related directly to the study of the function of LLMs. There are many dedicated tools such as Flourish that have a solid and valid place in academia, but those programs are not LLMs. Not only were my questions left unanswered, the question that I asked was one that could be answered easily with a simple Google search. I had wasted many more resources than I would have spent using regular internet searches.

Furthermore, inaccuracy is far from the biggest problem LLMs pose to the academic world. Within judicial circles, there is a doctrine known as the “Presumption of Regularity.” Under this presumption, United States courts automatically assume that any and all information presented by the United States government itself within a court of law is truthful. This serves to speed up certain processes, as well as make the federal government difficult to win a lawsuit against. However, in recent years, this “presumption of Regularity” has been faltering. recently, the Supreme Court has found that, due to a recent pattern of withheld truths and unwithheld falsehoods, the presumption of regularity can be paused in some circumstances. Within a similar timeframe, I have seen many professors grow weary of their students portraying the work of an LLM as their own. The “benefit of the doubt” as it were, is no longer routinely given. Conversely, I have spoken with many students who no longer trust their own professors, a few instances being students unsure if the equation they are being tasked with solving was generated with AI, and therefore, not truly solvable.

As this distrust grows and as LLMs become better and better at mimicking humanity, false accusations grow. In the article I’m Kenyan. I Don’t Write Like ChatGPT. ChatGPT Writes Like Me. Marcus Olang states that AI detection softwares “are not only unreliable but are significantly more likely to flag text written by non-native English speakers as AI-generated.” Much like any other large scale AI, these detectors are untrustworthy. There is, so far, no real solution to this problem, other than the wide-scale abandonment of LLM work offloading altogether.

Even if we disregard the erosion and potential destruction of trust in an academic, and potentially professional setting, it is a disservice to yourself to utilize and rely on such tools. As stated in the MIT preprint Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, “The LLM group’s participants performed worse than their counterparts in the Brain-only group at all levels.”

All AI generated portions of this blogpost have been designated above. No LLMs were used in any writing, planning, or brainstorming step of this blogpost. The built-in spellcheck was utilized.