Last modified: 2026-07-15 23:18:00 (PDT)
AI-powered coding assistants can dramatically accelerate and improve your work, but they require careful and responsible use. Lab members who use AI tools must adhere to the following guidelines.
Technology in Rapid Evolution
As of early 2026, AI coding assistant technology is changing extremely rapidly, and we are just beginning to figure out how to use these tools effectively ourselves. All information on this site should be taken with extra caution, as best practices and capabilities continue to evolve.
You are fully responsible for checking and validating all AI-generated code and content. AI tools can make mistakes, generate insecure code, produce incorrect logic, or suggest approaches that are inappropriate for our specific research context. Before using any AI-generated code:
Warning
Never blindly use AI-generated code without fully understanding it. If you don’t completely understand what the AI has suggested, take the time to learn or ask a colleague for help.
You must clearly state whenever you have used AI tools in your work. This is essential for transparency and reproducibility. Specifically:
Example code comment:
# The following function was generated with assistance from GitHub Copilot
# and has been reviewed and tested to ensure correctness
When using AI tools to generate content that borrows from or adapts existing sources, you must ensure proper attribution. AI tools sometimes paraphrase or adapt content from documentation, guides, or other resources without clearly indicating the original source. It is your responsibility to:
When instructing AI tools to create documentation or written content, explicitly request that they provide proper attribution for any borrowed or adapted material. For example: “Please quote from and paraphrase [source], with proper attribution” rather than simply asking it to summarize information on a topic.
When using AI tools to help develop journal articles and other academic writing, you must take special care to ensure transparency, maintain intellectual ownership, and avoid plagiarism. The following practices help achieve these goals.
Working with AI through GitHub creates valuable documentation of your contributions versus the AI’s. This track record can be crucial if reviewers or editors question your use of AI tools.
GitHub Pull Requests and Issues provide:
@copilot) made which changesThis transparency protects you if journal reviewers are skeptical about or opposed to AI use in research. You can point to the PR history to demonstrate that you maintained control and responsibility for the work.
Make your prompts explicit and detailed to establish that the ideas originate from you, not from the AI.
When requesting AI assistance with academic writing:
Example of a good prompt:
I need help writing the discussion section for a study on social determinants of health. My core argument is that [your specific argument]. The key findings I want to discuss are: [list findings]. I want to interpret these findings as suggesting [your interpretation]. Please help me draft this section while preserving these core ideas and citing relevant literature.
This approach creates clear evidence that the intellectual content came from you, while the AI helped with expression, organization, and literature integration.
Avoid vague prompts like “write a discussion section about my results” that give the AI too much creative control and make it unclear whose ideas are being presented.
Always instruct AI tools to identify and cite their sources to prevent unknowing plagiarism.
AI language models are trained on vast amounts of text, including published research. While they don’t have direct access to their training data during generation, they may produce text that closely resembles or paraphrases existing work without providing attribution. This creates a plagiarism risk.
Best practices:
Example request:
Please help me write this section, and explicitly cite any sources you draw from or adapt. If you’re paraphrasing from specific papers, identify them clearly so I can verify the citations.
Remember that even with these precautions, you must still verify the accuracy and appropriateness of any AI-generated citations, as AI tools can sometimes generate plausible-but-incorrect references (“hallucinate” citations).
A GitHub issue-and-pull-request workflow makes this transparency practical:
When developing content for journal articles, a similar workflow creates documentation that demonstrates responsible AI use and intellectual ownership. You can point to your GitHub history to show reviewers that you directed the AI rather than simply accepting its output.
When using AI for academic writing, also remember to:
These practices help you benefit from AI assistance while maintaining the integrity, originality, and credibility of your academic work.