Artificial Intelligence

Artificial intelligence has exploded onto the academic research scene in the past two years. With so many tools out there that incorporate AI in some fashion, it can be difficult to navigate this rapidly changing technology. This guide will focus on some basics of AI, the different forms it can take, pros & cons of using AI for research, and best practices for using AI.

As with all technology, you should default to your course instructor's policies regarding the use of AI on assignments and research.

Artificial Intelligence Terminology

The following section will briefly define terms that we often use interchangeably but in reality have very different purposes and meanings.

Artificial Intelligence (AI)

According to Oxford English Dictionary:

"The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this. In later use also: software used to perform tasks or produce output previously thought to require human intelligence, esp. by using machine learning to extrapolate from large collections of data. Also as a count noun: an instance of this type of software; a (notional) entity exhibiting such intelligence. Abbreviated AI."

Hallucination

"A situation where an AI system produces fabricated, nonsensical, or inaccurate information. The wrong information is presented with confidence, which can make it difficult for the human user to know whether the answer is reliable" (Carnegie Mellon University).

Chatbot

A chatbot is a program, sometimes hosted/downloaded directly to your device or used via web browser but requiring internet connectivity, that simulates human conversation. They can provide information, suggestions, writing help, or other types of assistance (Oxford English Dictionary).

It is important to note here that chatbots are not necessarily AI and have been around a lot longer than the AI we think of today. Just because you are using a "chatbot" doesn't mean it is an AI chatbot.

Generative AI

"Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data (NVIDIA.com).

Pros & Cons of Using AI for Research

Like all things, there are both upsides and downsides to using AI with your research and for the majority of us, it isn't "if" we use AI but "when" we use AI as it is quickly becoming a mainstay in academic research and proving to be very beneficial. The problems arise, however, when researchers do not check the content created by the AI. As defined above, hallucinations or hallucinating AI is a very real concern and happens often, as much as 27% of the time according to the New York Times. Because of hallucinations, which occur even more frequently with more complex questions, it is important to always review the content output of AI and to remember, AI is a tool that needs supervision and review. We do not simply accept the top 10 Google Search results as "fact" without reviewing the ones we actually want to use in our research. This is how it should be when using AI as well.

Pros of Using AI

Great for generating and organizing ideas.
Helping identify patterns and trends.
Simplifying complex topics.
Extracting references from articles.

Cons of Using AI

Can use plagiarized text in its answers.
Most do not offer citations to the sources of information it used to answer your prompts.
Hallucinations can lead to entirely invented information, including fake references when asked.

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