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Working with Generative AI

What is Generative AI?

 Artificial Intelligence is the capability of computer systems or algorithms to imitate intelligent human behavior. Generative AI is artificial intelligence that is capable of generating new content (such as images or text) in response to a prompt by learning from a large reference database of text or other information. Generative AI will also take existing text and paraphrase or rewrite it. AI makes predictions based upon patterns in the data it has been trained to access, but like any prediction a human can make it is simply the most educated guess the AI can make--which is not always as informed as we may expect. AI cannot perceive, reason, or think.

The AI system is trained, but like humans cannot know everything. AI Is trained using large amounts of data in a with intelligent algorithms that find and adapt to patterns in that data. Humans create and select the data and algorithms on which the AI is trained, so the depth and variety of data is curated based on intended use and the training method. AI is trained in three ways, Supervised Learning, Unsupervised Learning, and Reinforcement Learning:

  • Supervised Learning is training an AI receives using sample data within a specific labeled dataset to make more accurate predictions. Unsupervised learning lets the AI explore data and find patterns to make associations between data points before a specific goal is made. Examples include image and object recognition, identifying spam or banking fraud, and assessing customer experiences in an online store.
  • Unsupervised Learning is training an AI by letting it explore unlabeled data to find patterns and identify associations between data points without a specific goal. This can help isolate differences and anomalies as well as similarities. Examples include datamining & creating customer profiles, news feeds, and medical imaging spotting anomalies for patient diagnosis.
  • Reinforcement Learning is training an AI uses a trial and error method where the AI learns from its successful outcomes and makes predictions based on those "experiences." It is similar to supervised learning but it does not use sample data, instead it learns from real time input. Examples include safety improvements for  self driving cars, language processing & text summaries, and financial predictions like stock prices. 


Does Spell Check Count?

You may be familiar with autofill/autocorrect tools, such as Microsoft Word or Grammarly for spell check (this is different from Grammarly Go, which IS generative), Google Search boxes that finish your sentences, or predictive text when messaging friends.

These are all types of artificial intelligence tools, however they are NOT generative! These tools do not create brand new or original material based on a prompt. When your professor tells you not to use generative AI, it is generally ok to still use spellcheck and autocorrect, but you should always check with them to be sure. 

Additionally, language translation tools like Google Translate are not considered generative as they are not creating new material, they are simply translating existing material. There are generative language AI out there, such as within Google Cloud AI, but this is different from the traditional translator. 

Responsible Use of AI

AI can be useful for many different things, like brainstorming, topic creation, idea generation, or suggesting keywords to use in library database searches. However, this is a tricky area to navigate and should be explored with caution. 

ALWAYS check with your professor before using AI in any assignment! Artificial intelligence is a rapidly evolving field, and applies in many different ways across the disciplines. Each course you take will have its own policy on AI based on that course's learning objectives. However, it is best to keep in mind that submitting anything written using generative AI, especially without the explicit permission of the professor, could put you at risk for accusations of plagiarism.

Artificial intelligence is a tool, you as the human student are still the brain power behind the final product of your work. AI is not here to do your learning and homework for you! 

Evaluating Output

Artificial Intelligence is not rational, and cannot evaluate quality on its own the way a human can. Like humans, there are gaps in the AI's knowledge as such as in language translation. 

Please bear in mind that AI can reflect cultural biases and hallucinate. AI sources it's information from across the internet. This can result in output reflecting racism, sexism, homophobia, or outdated or even entirely false narratives or information.

AI hallucinations happen when the software makes up its own answer that can sound very official, but is ultimately nonsensical or damaging. Furthermore, AI can manufacture false article citations for studies that don't exist, so be sure to follow up thoroughly on studies and sources.

It's always a good idea to evaluate generative AI output. Use techniques such as Lateral Reading to evaluate generated output by crosschecking with outside sources. 

Citing AI Output

Whenever you use AI output, you must cite it just as you would any other source. Citing sources is a crucial part of the research process, and it can be tricky figuring out how to cite a non-traditional source like AI and ChatGPT, especially since those outputs are not retrievable by other users.

In addition to the citation, some professors may want you to include a copy of the prompt you used and a transcript of your conversation with the AI. 

Here are guides to four styles used at WNE: 

AI in Teaching & Scholarship

Generative AI can play a role in the research process. Tools like Research Rabbit, Scite and Elicit can assist with discovery, literature mapping, and evaluating sources. Professional organizations such as the APA have also developed guidelines for using and disclosing AI within published content. 

Artificial Intelligence can guide your research process by providing alternative perspectives or guiding the creation of your research question and keywords. The toughest part of research is narrowing down a question and the keywords to help find the answer. AI tools can help provide material from both sides of an argument, key phrases to guide your research, and a simple way to start your exploration into database resources. 

Generative AI also has uses within the classroom. The Center for Transformative Teaching at University of Nebraska-Lincoln has an excellent guide on using AI in teaching, including example course policies, prompts, and assignments. 



“ChatGPT Has Changed Teaching. Our Readers Tell Us How.” The Chronicle of Higher Education, 11 Dec. 2023,

David, L. (2019). Understanding artificial intelligence ethics and safety. The Alan Turing Institute.

“Definition of ARTIFICIAL INTELLIGENCE.” Merriam-Webster, 22 May 2024,

“Definition of GENERATIVE AI.” Merriam-Webster, 21 May 2024,

Guidance for Generative AI in Education and Research - UNESCO Digital Library. Accessed 29 May 2024.

Hartman-Caverly, Sarah. Library Guides: Hidden Layer: Intellectual Privacy and Generative AI: Hidden Layer Workshop. Accessed 29 May 2024.

“How AI Reduces the World to Stereotypes.” Rest of World, 10 Oct. 2023,

How to Use Elicit Responsibly | Ought. Accessed 29 May 2024.

IBM Technology. What Are Generative AI Models? 2023,

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Perkovic, Ines. McMaster LibGuides: How Do I Cite Generative AI?: Vancouver. Accessed 28 May 2024.

Subbaraman, Nidhi. “Exclusive | Flood of Fake Science Forces Multiple Journal Closures.” WSJ, 14 May 2024,

What Is AI Model Training and Why Is It Important? Accessed 30 May 2024.