Making a talk, without and with AI

Some of the discussion online has been about how not to use AI in making academic talks (see, e.g., this post by Jessica Hullman). A junior researcher asked my opinion on using AI to help make slides and posters. I shared my thoughts with them, which some others might find interesting.

I’ll first give my take on the value of making talks by hand, which I think I’m not bad at. I’ll then comment on how AI could fit into the process well or poorly. I will say that I’m not (yet?) a wizard at using AI to enhance my talks, so don’t come here expecting pro tips (except, spoilers, that the process of writing itself is immensely important). Since AI is a fast-moving topic, note that this post was originally written in May 2026, but I don’t think that the general principles here are very dependent on the current AI capabilities.

Making talks by hand

Roughly speaking, the contents of a talk can be decomposed into three parts. From highest to lowest level, they are:

• The story you’re telling the audience
• The contents of each slide
• The words you actually say

Before you even make a single slide, you should already have a good idea of the story of the talk. This includes things like, what is the motivation for studying this problem, which are the important related works to mention, what technical results to highlight, etc.

Designing the story is the single most valuable part in preparing any talk , both in terms of your intellectual engagement with your own research and making sure you give a coherent and educational talk. Every one of these details requires thought and consideration. Why should people care about this problem? What examples illustrate the problem cleanly and intuitively? Which technical results are the most interesting, and how should they be simplified for the purpose of a talk?

Note that all of these story design choices are highly subjective. There is no “one talk” to give about a particular result, and two co-authors of the same paper may prepare and give totally different talks. That type of variance is fine and by design. A talk is meant to give your own perspective on the research results, and that should come through in the story you choose to tell. This is why you shouldn’t rely too heavily on slides from talk already given by a co-author: it’s their story, it doesn’t have to be yours.

The process of designing your story will also help you understand your own results better. Thinking about how to motivate a problem will help you discover new connections. Thinking about a simple example will help you understand the nature of the phenomenon you’re demonstrating. Thinking about which results to present and how to do it (e.g., what to simplify) will help you distill the core technical ideas in your work. The important part is not only the final content on your slides, but it’s the thinking and your process to get there.

Once you design and internalize your story, it will be useful in other places as well. For example, if you haven’t written the paper yet, it will make the introduction much easier to write. And if someone asks you to explain your result impromptu, you’ll be able to explain it more clearly to them.

Thus far, our discussion has been about designing the story. I often find that, when you have a sufficiently clear story in your head, writing it down is very easy. But there is another disconnect, between the words and pictures on the slides and the actual words you say. This is where practice and rehearsal comes in: you’ll frequently find that the most natural way to present a slide is entirely different than what you put down in the first place, and you have to go back and refine the slide afterwards (and potentially iterate). This is another reason why using someone else’s slides is a common pitfall: the way that is natural for you to present a result is not the same way that is natural for them.

It’s not the main point of this post, but a brief comment on delivery. It should be clear to the audience that you care about what you’re presenting about. After all, if you don’t care, why should they? This is of course challenging for some people, who are not naturally charismatic or are afraid of public speaking. This is a core part of academic research, so it will benefit you to improve at it.

As a final point here, in case it’s not obvious, why is giving good talks an important thing to do? In some sense, being a researcher is being an influencer. Research is inherently an attentionally-driven field. You can have the most brilliant ideas and results, but if no one knows about or understands them, then the value of the research is a fraction of what it could be. A talk focuses all the attention on you and your results, so make the most of the spotlight when you have it.

How can AI help?

I’ll start by outlining ways that AI can hurt you when preparing talks, but end with some suggestions on how it can help you make better talks than you made before.

Using AI too broadly can be disastrous. Suppose you use it to design your entire talk. Then you are completely skipping the sometimes-painful but essential process of coming up with the story. You won’t understand your research as deeply. You won’t have internalized it as you would have if you sweated through it, and you won’t be able to explain the research as clearly in settings when you don’t have AI at your fingertips.

Using AI too heavily also discards a lot of you from your talks. People came to the talk not only to hear about the research, but for your perspective on the research. The authenticity is very important (and, I believe, will only become more important over time).

This ties into another point, about delivery, where it is important to show that you care about your research. The easiest way to show you don’t care about your research is to dump slop slides (i.e., only lightly edited AI outputs) at your audience. You expect me to believe this is something you care at all about if you can’t spend just a few hours preparing a decent talk? It also signals that you have no respect for your audience. If I catch you presenting slop slides, you will correspondingly lose a lot of my respect for many years.

AI can also result in slides that have a big disconnect in the content on the slides versus what you would actually say. It’s effectively like having someone else give you their slides. The “AI’s voice” is likely to be entirely different from yours. You can of course refine and iterate on them, but at that point, you might as well just make the slides yourself.

So with this said, what can AI be useful for? AI can be help by giving a second opinion on things. Maybe you’ve done the thinking yourself already (which, as mentioned before, is the important part), and you want to see if there’s any other perspectives you didn’t consider. It might suggest some other ideas that you already thought of and discarded, or other new ones that you hadn’t considered. But you should really think of it as being a second opinion, your own perspective and preferences should still dominate.

AI can also help make content that you wouldn’t have the time or the skills to make before. For example, AI can help make high-quality visualizations, animations, and interactive demos. These tools can help communicate an underlying technical idea much better than just words, but could previously have been too difficult to actually create.

AI models are not going away. You should use them to make your talks better: communicate and express your underlying message in a way you would not have been able to do before. But you should not use them in a way that makes your thinking or your talks worse: stealing away your valuable thoughts, removing what makes you you from your talks, or embarrassing yourself in front of your professional colleagues.

By Gautam