AI is an amazing research tool. But it is also a tool that has been adopted with such speed in research and writing practices that we’ve had no time really reflect on the questions: What does it actually do to our creativity? Our critical thinking? Or, more specifically, the ability to craft an argument for a CHI paper?
Beyond having consequences for your thinking, it can also have consequences for your career if you’re not up to date on the relevant guidelines of AI use.
With that in mind, here are some practical tips for AI use related to CHI submissions, but also a few general points, relayed from other professors, which I find to be good guiding principles.
As this topic is constantly in flux, it is important to state the disclaimer that these tips here are gathered in September 2026.
Tips from professors to students
Here is a nice post by Eytan Adar about some of the consequences of growing up as a researcher with GenAI:
In the end of the essay, he’s got some good tips for students:
- Use GenAI proportionally to how well you know something. The temptation to use it for things you find hard will be high. GenAI will also feed your productive procrastination habits. Fight it.
- Know which parts of your work are load-bearing. Can you defend a choice, state what would falsify your result, or explain why this analysis and not another one? These are load-bearing decisions and need to be yours.
- GenAI is sycophantic. It will tell you what you want to hear and won’t tell you when it’s wrong.
- Ask others to help you with accountability. Your mentors and peers can help keep you honest in your use of GenAI.
Here’s Niklas Elmqvist’s comprehensive guide on how to use AI in HCI research:
How to AI: A Guide for HCI Researchers
I am going to relay this excerpt addressing students:
Have Some Pride
You are a Ph.D. student, postdoc, or researcher because you are capable of original thought. Do not trade it away for convenience. It truly is a devil’s bargain.
Current cloud-based LLMs are marketed as reasoning at Ph.D. level. But what does it mean for your identity as a researcher if you start acting as though it did? You are not here because you are good at prompting. You are here because you have developed creativity, lateral thinking, judgment, taste, and expertise that took years to build. Even your very human quirks should be valued. Use AI as you would any powerful tool: deliberately, with awareness of what it can and cannot do, and without letting it make decisions that are yours to make.
Here’s Daniel Buschek’s blog post on writing patterns to avoid – often incurred by overuse of LLMs for writing:
The four writing issues that he highlights are:
- Marketing Language
- Performative Related Work Section
- Incorrect Representation of Cited Work
- Stretched Result Summaries
Check out his full essay for examples.
ACM author policy on AI
I’ll end on a few practical notes regarding the ACM author policy on responsibility and disclosure of AI use.
As of the latest ACM author policy, we do not have to disclose use of AI for writing, only when used for doing research and producing other artifacts for the paper. You can read the current policy here: https://www.acm.org/publications/policies/new-acm-policy-on-authorship
But here’s the important excerpt (as of Sept 2026):
Re: point 1:
Here’s a nice tool that helps you to draft a paragraph on AI disclosure for your paper (shared with CHI ACs on an internal channel):
Re: point 2:
Despite not having to disclose AI use for writing, remember that we take responsibility for all the writing we submit/publish.
As a pratical tip, beyond the ones shared by other professors above, you should be particularly worried about fabricated references if you’ve used AI for literature search.
As an example: if you have used AI to find relevant references, it is very bad practice to cite them without reading those papers… But even worse if you have not even checked whether these references actually exist! Apparently, hallucinated references is a big problem these days, even in fully peer-reviewed and published papers – Nature article: “More than 140,000 fake citations across four research repositories were identified in papers and preprints published in 2025 alone” .
And there are several news stories about how this has affected researchers’ careers.
So if you have used AI for finding relevant literature (AI can be a great tool for that!), then make sure:
- that the paper exists (and the BibTex reference is valid)
- that you read it, of course, so that you can cite it correctly
🙂