ChatGPT and GenAI

ChatGPT and other generative AI (GenAI) writing tools can generate convincing and unique text when prompted. Because this content is difficult or impossible to distinguish from human-authored text, it's important for students, instructors, and researchers to have a shared understanding of how these tools can or should be used in a university environment. These are general guidelines to help the UW community understand GenAI; faculty members, departments, and administrators are encouraged to discuss and establish discipline-specific practices. Also note that the focus of this primer is on text GenAI, but similar AI tools exist for other formats as well, such as images, video, computer code, etc.

Generative AI Explained

ChatGPT and other GenAI tools use large language models (LLMs) and machine learning to produce text that appears, at least initially, to have been written by a human. To accomplish this, they are first "trained" on a large amount of human-generated content (like books, Wikipedia, websites, etc.). During this training, they are programmed to recognize patterns in how the text is written and the probabilities that certain words occur together. These patterns and probabilities are then used to generate new text that replicates the style and content of human writing, based on a user prompt.

ChatGPT will output text that resembles the text found in its training materials, but the amazing thing is that, due to how the text is constructed around patterns and probabilities, it can also generate completely new and unique combinations of words that don't appear anywhere in the training, but still follow the same patterns of human-generated text. This is how it can generate text of almost any kind about almost any topic.

Limitations of Generative AI

GenAI is a tool for generating unique text out of the language patterns in a large collection of human-written content. It does not "look up" information or verify its outputs. So the outputs generated by GenAI should always be critically evaluated. While the capabilities of GenAI are impressive, there are certain factors that, at least for now, limit its uses:

It's not up-to-date: GenAI will only be as current as its last training date. Prompting it about current events or developments after this date will yield incorrect and unreliable results.
It's not a search engine: While some GenAI tools will integrate with search engines, by default the text generated will not draw from Internet searches in a way that human authors will.
It doesn't have experience with the physical world: because its outputs are generated only from patterns in existing writing, it's often unable to produce descriptions of things that have not been described in text.
It struggles with factual information and quotations: GenAI does not verify its own outputs like a person would. For this reason, it may produce incorrect statements with no basis in fact, or invent information, quotations, or references to non-existent works.
Some topics/concepts/languages are not reflected in the training data: GenAI can only output reasonable text for things that are adequately reflected in the data. Certain prompts will yield nonsense if there is not a mass of text from which it can draw patterns.
It has built-in limitations: issues around sensitive topics, privacy, copyrighted works, and intellectual property rights have forced the designers of GenAI tools to add limitations in the programming to avoid legal troubles.

University Guidelines and Policies

The University of Winnipeg does not have an overarching policy regarding the use of generative AI for learning or research. It is the responsibility of each course instructor to determine how best to integrate these tools into the course, if at all, and to create guidelines for acceptable use of AI in the course. It is also the responsibility of each course instructor to communicate these guidelines clearly to students so they understand their responsibilities in the course.

While there is no single UW policy related to AI in learning and research, here are some relevant policies:

Academic Misconduct Policy: unauthorized use of AI tools to complete assignments would fall under the Improper Research/Academic Practices part of this policy.
Dean's Recommendation for Course Outlines: under point 12, students must abide by the rules created by their instructors. This recommendation also includes an example of MLA citation of AI text/uses.

Using Generative AI for Research

NOTE: Before using any type of AI for learning or research, it's very important that students confirm with their instructor that this use is permitted in the course.

Due to the limitations in their design, GenAI tools are not suitable for identifying sources of information. They are known to produce fake sources, and the real sources they identify are often not of good academic quality. However, generative AI may be useful at different stages in the research process, as an assistant. Depending on your need, you may find AI useful for:

brainstorming interesting topics
identifying alternative keywords or synonyms
developing a search strategy
summarizing a text
simplifying a complicated text

Some library databases have started incorporating these features into their search tools, to help students and researchers make the most of their efforts.

If you use AI as part of an assignment, you must cite and reference it correctly. Here are the recommendations in various academic writing styles:

Using Generative AI in the Classroom

As stated, instructors are responsible for developing and communicating guidelines about AI use in their courses. Here are some course and assignment design principles to consider when developing a course.

Discuss GenAI tools with your students: Make sure you and your students understand what GenAI text tools are and how they work. Discuss the ethical and unethical uses of GenAI with your class. Agree on guidelines for the ethical use of artificial intelligence with your students.
Model acceptable uses of GenAI: Demonstrate to your students the uses of GenAI that you consider acceptable for a given assignment, such as brainstorming or generating topics/ideas, and hold yourself to same standard as you do your students.
Demonstrate the limitations: Show students where GenAI text falters (generating citations and references, math/logic, current events, factual information, etc.)
Critique the outputs: Generate text on a relevant topic and have students critique and fact-check the output.
Encourage keeping drafts: Ask students to keep the drafts of their writing (or set up automatic version history) and/or require them to produce a research log, so they can demonstrate their research and writing process.
Promote authentic writing: Encourage your students to find ways to demonstrate the authenticity of their writing. Discuss with them how GenAI text differs from human writing, and provide them with examples.
Use in-class writing: Have students complete writing assignments in class to eliminate the possibility of them using AI for that text. Keep in mind that many students feel more comfortable writing and editing than having to produce text quickly and spontaneously, so avoid using this for every assignment.
Use oral presentations: Have students deliver oral presentations, either as a standalone assignment or in conjunction with written assignments. This can serve to verify student understanding. Again, many students will feel uncomfortable with this and so it should not be overused.
Use experiential and service learning: Include assignments that are connected to individual experiences in the real world. These will be harder (though not impossible) for GenAI to complete. This has the added benefit of showing how learning can be applied to students’ social lives and used to improve their communities.