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AI Resources for Faculty: Home

This guide is designed for faculty to use to understand and effectively use Artificial Intelligence in their courses.

Introduction

What comes to many people's minds when they think of Artificial Intelligence (AI) is actually Large Language Model Artificial Intelligence (LLM's or LLMAI's). These types of AI are part of Generative AI, meaning they generate human-like content in the form of text, images, and, increasingly, video. LLM's are trained on huge sets of data, mostly pulled from the internet, though some LLMAI models can be trained on more specific data for specialized tasks.

The following definition is from ChatGPT 4o:

LLM AIs (Large Language Model AIs) are powerful computer programs that can understand and generate human language. Imagine it like a super-smart robot that can read, write, and answer questions by learning from tons of text, like books, websites, and articles. It doesn’t think like a human, but it can give useful responses based on the patterns and information it has learned. (Response generated October 7th, 2024).

One thing to keep in mind about LLMAI's is that they do not actually know anything. In simple terms, responses from a LLM is them sifting through their data and go through other responses found in their data. Then they string together words that appear most common to create a seemingly coherent, factual-sounding response to a user's input. LLMAI's are simply guessing at the correct response, which can often bring about an AI phenomenon called "hallucination".

An AI hallucination is where the LLM makes up facts in an effort to create a correct response to a user's input. LLMAI's can really be thought of as over-eager assistants-- they want to please the user as quickly as possible, but sometimes will get in their own way. This is why it is important to have at least a base-line understanding of the topic that you are questioning the LLMAI on or to fact-check an LLM frequently. 

But how we question an LLMAI can be just as important. There are many techniques to create inputs for LLM's to get an appropriate response. The process for getting these inputs is called Prompt Engineering.

In January, 2025, Ethan Mollick assembled a good introduction to the major general-use AI chatbots.  

We can only offer some generalizations that might help put your own experiments in perspective. The below list assumes a conversation consisting of 1-3 human prompts and AI replies.

  1. Free AIs can produce text and images, plausible (if not always truthful) narratives, descriptions or arguments that are suitable submissions to many undergraduate writing assignments. AIs requiring a paid subscription can produce video animations, slide deck presentations (including slides and scripts). The latter generally produce work of better quality in images or text, too.
  2. The AIs are trained on information openly available on the internet, and increasingly some that is limited-access (requiring a subscription). The chatbots wield encyclopedic knowledge of historical events, social, political, and philosophical concepts or issues, economics, and culture.
  3. AI excels at foundational tasks: recalling facts and definitions (available on the web), comparisons, summarizing theories or specific texts, and explaining procedures, laws or principles.
  4. The chatbots might have considerable knowledge of literature that is not available on the web, if there is a lot of secondary sources or conversations on the internet about it. For example, ChatGPT might know themes, events, timelines and even details in Eugene Sledge’s book With the Old Breed, although that book is in copyright. This is because the book is a popular memoir that has informed popular media such as the miniseries The Pacific. However, the AIs might have only vague knowledge of a novel such as Marcus Goodrich’s Delilah, which was not digitized.
  5. AIs may be aware of, but not (yet) have good command of individual sources in archives that have not been digitized, or articles behind paywalls, such as in subscription publications or academic databases.
  6. AIs have become quite good at answering multiple-choice questions, even focused on specialized or arcane content.
  7. AIs can write fictional accounts that are solid if not usually very original. They can often, but not always, create hypothetical examples of theoretical concepts.
  8. AIs will be less capable of analyzing complex, real-world circumstances in the past or present. They can struggle to identify important arguments or build detailed interpretations around particular pieces of evidence, especially if the latter are not in their training data.
  9. When AIs are unaware of essential information to answer a prompt they may indicate that, or they may fabricate (hallucinate) details. They are designed to simulate conversation, which is not precisely the same as becoming authority committed to truth.
  10. AIs occasionally botch calculations and computer programming.
  11. In response to single or simple prompts, AIs frequently answer with vague or obvious observations that lack depth or detail.
  12. AIs can read and summarize documents (such as .pdfs) provided in a prompt, but may struggle with details in the documents.
  13. AIs excel at writing boilerplate or formality content, for example proposals, business plans, or courtesy correspondence.

Interested in Learning More?

Greater depth: What are LLM AIs?