Acknowledgement of AI use: recommended practices

Research presentations/publications

Norms for acknowledging the use of AI tools in academic scholarship are continuing to evolve (Resnik & Hosseini, 2025 discuss this in more detail). Attribution, rather than citation, is currently considered the clearest path to transparency in academic and research work. In scholarly writing and presentation, the exact form of attribution varies by discipline. One possibility is the inclusion of an acknowledgements section or slide. Attribution can also include licensing information, multiple hyperlinks, and a detailed description of how and why the material is being attributed in the work.

Journal editors and publishers may have specific requirements regarding permissible use of generative AI and practices for acknowledging use. Always check with your editor or publisher regarding these requirements.

In the absence of specific guidance, the following attribution framework, called the AI Disclosure (AID) Statement, developed by Dr. Kari D. Weaver at the University of Waterloo, can be used to acknowledge the use of AI tools in research publications and presentations.

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. This section provides details about the AI tools used, including their versions and dates of use. Following this, the statement should include only the relevant headings that describe specific ways AI was used in the project. Each heading should be paired with a statement explaining how AI contributed to that aspect of the work. If a heading from the list below is not applicable, 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.

Research publications/presentations section adapted from "Artificial Intelligence Disclosure (AID) Framework" by Dr. Kari D. Weaver, University of Waterloo Libraries. CC-BY-SA 4.0 licence.

AID statement example

Artificial Intelligence Tools: Gemini 2.0 Flash, ChatGPT v.4o, o1, and 4o deep research mode; Conceptualization: Gemini 2.0 Flash was used to revise the initial scope and refine research questions; Information Collection: ChatGPT deep research was used for summarizing and extracting information in the initial literature review process. Data Collection Methods: Gemini was used to generate synthetic data for pilot testing and to inform the design of data collection instruments; Privacy and Security: no identifiable data was shared with the AI tools during the study; Visualization: ChatGPT was used to suggest visualizations based on the dataset's structure and analytical goals to explore potential ways to present the findings. Writing—Review & Editing: ChatGPT and Gemini were used to provide grammar and style revisions; Project Administration: ChatGPT was used to draft project timelines and summarize meeting notes.

Additional resources

Links to publishers’ policies on AI use (Purdue University)

Recommendations for a classification of AI use in academic manuscript preparation (The International Association of Scientific, Technical & Medical Publishers)

Resnik, D. B., & Hosseini, M. (2025). Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary? Accountability in Research, 1–13. https://doi.org/10.1080/08989621.2025.2481949

Weaver, K. D. (2024). The artificial intelligence disclosure (AID) framework: An introduction. C&RL News, 85(10), 407–11. https://crln.acrl.org/index.php/crlnews/article/view/26548