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Citation Styles & Tools

A brief explanation of citation fundamentals and a guide to useful resources.

Citing Generative AI

Generative AI is a type of Artificial Intelligence (AI) that uses machine learning (ML) to produce new content from an extensive training dataset. Some platforms you may have heard of include ChatGPT, Microsoft Copilot, or Google's Gemini. Because it's main purpose is to generate content, it's important to be mindful of whether its application is appropriate for a specific academic setting. 

What is it good or not good at?

GOOD FOR NOT GOOD FOR
Summarizing Critiquing
Brainstorming or generating ideas Judging
Outlining Ethical choices
Shallow analysis Understanding social interaction

What you need to know about GenAI and Citations?

Using generative AI tools is very tempting for projects that take time, deep thinking, and may require skills you're still developing. If you aren't sure how to get started, using a tool to do the task for you can feel like a win for getting it done. However, be mindful when using generative AI that you're not using it to replace the learning elements of your assignments. If you aren't sure how to find sources, talk to a librarian!

Some issues with generative AI include: 

  • Academic integrity - Understand what is or is not allowed for a specific class. Using AI for writing and submitting it as your own if these tools are not part of your assignment creates an academic integrity issue. 
  • Fake citations - Tools like Chat GPT can generate pretty much anything you ask for, including citations to sources that don't actually exist. These "halu-citations" can lead to frustration and dead-ends. Don't use generative AI to do your research.
  • Not reproducible - Because chat sessions are different for different people and profiles, what an algorithm tells you in one session isn't reproducible for someone else. Facts should be provable and discovering them is repeatable, which is not something you can expect from a generative AI tool.
  • Ethical concerns - Generative AI can perpetuate bias, misinformation, or harmful stereotypes because it is necessarily trained using biased data. There are also environmental concerns, as AI uses significant amounts of water during operation.

How to cite AI if it's allowed in a class?

So, you checked the class policy and have found that generative AI is allowed if it's cited. How to cite it is handled differently based on what formatting style guide you're using. Check with the accepted style for your class to see how to handle it based on the style guide you're using for an assignment:       

Appropriate formatting is also evolving and you may want to check back frequently!

The AID Framework: Attribution and Disclosing AI Use

Attribution is a less formal approach for crediting ideas and material from others. In scholarly writing and presentation the exact form attribution takes varies by discipline. A widespread example is the inclusion of an acknowledgements section or slide. Attribution can also  include information beyond the direct citation, including licensing information, multiple hyperlinks, and a detailed description of how and why the material is being attributed in the work. With the current state of generative artificial intelligence systems, direct citation may not fit a specific use case, and attribution offers a path to transparent disclosure across academic or research work.

The Artificial Intelligence Disclosure (AID) Framework (2024) was developed by Kari D. Weaver, Learning, Teaching, and Instructional Design Librarian, to enhance transparency and consistency in attribution practices for generative artificial intelligence. Inquiries may be directed to kari.weaver@ocul.on.ca.

 

This resource is an adaptation of the Artificial Intelligence Disclosure (AID) Framework (2024) by Dr. Kari D. Weaver, University of Waterloo Libraries. It has been produced as allowed under its assigned CC-BY-SA 4.0 license. 

The purpose of the Artificial Intelligence Disclosure (AID) Framework is to provide brief, targeted disclosure about the use of AI systems based on the range of activities used for research writing. The AID Statement is appended to the end of the paper (similar to an Acknowledgements section), detailing the AI tools used and the manner in which they were used, based on the possible points of engagement through the writing process, as captured in the headings. The formatting is intended to be both human- and machine-readable, and uses the following structure:

AID Statement: Artificial Intelligence Tool: [description of tools used]; [Heading]: [description of AI use in that stage of the work]; . . . 

Each heading: statement pair will end in a semi-colon, except for the last statement, which will end in a period. Any other symbols can be used in the "statement" portion of the heading: statement pair except for colons and semi-colons.

If AI tools were used at any point in the writing, research, or project management processes, the AID Statement will always begin with the "artificial intelligence tool" section. It will then by followed by any heading: statement pairs necessary to disclose AI tool use. Heading: statement pairs will only be included if AI was used in that portion of the writing process. If a heading is not needed, it should not be included. If AI was not used at any point in the writing, research, or project management processes, authors would not include an AID Statement in their work.

The potential headings for the AID Statement, and their definitions, are the following:

  1. Artificial Intelligence Tool(s): The selection of tool or tools and versions of those tools used and dates of use. May also include note of any known biases or limitations of the models or data sets.
  2. Conceptualization: The development of the research idea or hypothesis including framing or revision of research questions and hypotheses.
  3. Methodology: The planning for the execution of the study including all direct contributions to the study design.
  4. Information Collection: The use of artificial intelligence to surface patterns in existing literature and identify information relevant to the framing, development, or design of the study.
  5. Data Collection Method: The development or design of software or instruments used in the study.
  6. Execution: The direct conduct of research procedures or tasks (e.g. AI web scraping, synthetic surveys, etc.)
  7. Data Curation: The management and organization of those data.
  8. Data Analysis: The performance of statistical or mathematical analysis, regressions, text analysis, and more using artificial intelligence tools.
  9. Privacy and Security: The ways in which data privacy and security were upheld in alignment with the expectations of ethical conduct of research, disciplinary guidelines, and institutional policies.
  10. Interpretation: The use of artificial intelligence tools to categorize, summarize, or manipulate data and suggest associated conclusions.
  11. Visualization: The creation of visualizations or other graphical representations of the data.
  12. Writing – Review & Editing: The revision and editing of the manuscript.
  13. Writing – Translation: The use of artificial intelligence to translate text across languages at any point in the drafting process.
  14. Project Administration: Any administrative tasks related to the study, including managing budgets, timelines, and communications.

Example for use in research

Artificial Intelligence Tool: Microsoft Copilot (Central Washington University instance); Conceptualization: Microsoft Copilot was used to revise and refine the central research question; Data Collection Methods: Microsoft Copilot was use to draft the first version of the survey instrument; Data Analysis: Microsoft Copilot was used to identify themes as themes coded from collected survey responses; Visualization: I used Microsoft Copilot to generate a line chart comparing information gathered over time; Writing - Review and Editing: Microsoft Copilot was used to help break down my results and discussion sections into smaller paragraphs; Project Administration: I used Microsoft Copilot to create a research and project submission timeline and list of tasks for the project. 

 

Example for use in education

Artificial Intelligence Tool: ChatGPT v.40 and Google Gemini 3; Methodology; ChatGPT was used to aid in study design and revise study structure; Information Collection; I used Google Gemini to collect relevant journal articles and other resources; Interpretation: Google Gemini 3 was used to explain the some research concepts related to Freudian theory; Writing - Review and Editing: Paper manuscript was reviewed by Google Gemini 3 for conciseness and clarity.