# Workflow 03 — Choosing your design

**When:** weeks 3–5. **Feeds:** the proposal (Oct 11) and the implementation milestone (Nov 15).

## Goal

Decide **what kind of project this is**, and make the design choices that let it actually answer your question.

## The three kinds of project

The syllabus allows projects that are **theoretical, empirical, or argumentative**. Pick the one that best fits your question. You can combine them, but one should lead.

| Kind | You deliver | What makes it good | Example (illustrative) |
|---|---|---|---|
| **Theoretical** | An argument about *why* or *how* something happens, with a mechanism | A clear mechanism, a claim that could be wrong, stated scope conditions | Why AI adoption may concentrate power in national rather than local governments |
| **Empirical** | Evidence about what IS happening: a description, comparison, or test | A defined unit and sample, a real comparison, honest limits on the claims | Coding 200 AI-related bills for which interests they protect |
| **Argumentative** | A case for what SHOULD be done, with explicit criteria | An explicit standard (equity, efficiency, rights, feasibility), and the strongest objection answered | Whether cities should require water-use disclosure before approving data centers |

## Example projects

**You propose your own project.** The examples below show the range of what's possible. They're **not a menu**: you don't have to pick one, and many good projects look nothing like them. If one is close to what you want, make it your own by narrowing it to a specific place, period, case, or user.

| Area | Example | Kind |
|---|---|---|
| Public opinion | How does telling people a political message was AI-generated change how persuasive they find it? (Piloted on a synthetic sample; see Workflow 02) | Empirical |
| Campaigns | How did campaigns in one recent election cycle use generative AI in ads, fundraising emails, or voter contact? | Empirical |
| Regulation | Should states require disclosure of AI-generated content in political ads? What would a good rule look like? | Argumentative |
| Firms | Why do some AI companies back regulation of their own industry? A mechanism, and the cases that would test it | Theoretical |
| Data centers | Who wins and who loses when a county approves a data center? A comparison of two local permitting fights | Empirical |
| Geopolitics | Do export controls on AI chips achieve what their designers intended? An evaluation against the stated goals | Argumentative |

These are starting points, not questions ready to go. Each one still needs Workflow 01 to become a specific, answerable question.

## You produce: a short design paragraph

A paragraph that answers three questions:

1. **What kind of project is this**, and why does it fit your question?
2. **What are your units and cases**: what one observation, case, document, or user is, which ones you cover, and why those?
3. **What would prove you wrong**: the result, case, or failure that would undermine your claim?

Plus the strongest rival explanation or objection you'll address (step 3).

## Steps

1. **You.** Read the three rows in the table above. Pick the kind of project that leads, and write one sentence on why.
2. **You.** Answer: *what is one row in my data?* (Or one case, one document, one test input.) Then: *why these cases, and not others?* If the honest answer is convenience, say so.
3. **With AI.** Find the rivals, and check the design:

   ```
   You are a research-design coach for an undergraduate AI-and-politics course.
   My project is [theoretical / empirical / argumentative]. Here is my question, my units and cases.
   1. Check that my unit of analysis matches my question.
   2. Ask whether my cases are systematically unusual, and what that would do to my conclusions.
   3. Suggest the 3 strongest rival explanations or objections to my likely claim,
      each stated the way its supporters would put it.
   4. Suggest what my comparison or standard could be: what is compared to what (empirical),
      what my mechanism predicts that a rival doesn't (theoretical), or which evaluative criteria (argumentative).
   5. Ask me what result would prove me wrong. If nothing could, tell me.
   ```

4. **You decide.** Choose the rival or objection you'll take on, and your comparison or standard. Write what would prove you wrong *before* you look at any results.
5. **You.** Write the design paragraph in your own words. It becomes part of your proposal and your implementation milestone.

## Check before moving on

- [ ] You can name your kind of project, and what "good" means for it.
- [ ] Your units, cases, and why you chose them are written down.
- [ ] There's a comparison or criterion that gives your project leverage.
- [ ] You've picked the strongest rival or objection, stated fairly.
- [ ] You've written down, in advance, what would prove you wrong.

## Common traps

- **Trying to do all three at once.** Pick the lead kind.
- **Descriptive work "to avoid making an argument."** Choosing what to describe *is* an argument.
- **Straw-man rivals.** If you can't state the objection as its supporters would, you can't answer it.

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*Project kinds from the POLISCI 158 syllabus, Project-Based Assessments.*