Week 13 — Speech Workshop / Rehearsal Studio · "Spot the Fallacy, Build the Argument"

Course: Public Speaking — Fundamentals of Oral Communication (COMM 1) · Silver Oak University (fictional sample) · Prof. Marchetti
Objective: Objective 7 — evaluate and construct arguments; identify logical fallacies in described arguments · SLO B (critical analysis) · SLO A (compose — build a sound argument)
Worth 50 points · Speech Workshops group = 15% of the grade · Workshop 13
Format this week: a concept-analysis workshop — you assess written arguments and build a written argument, then reflect. This week's self-assess step is the written argument analysis (no recording required, though the optional reflection in Part 7 invites you to say it aloud if you choose).

This is the course's signature weekly component. Every instructional week has one Speech Workshop. This week's drill matches the week's objective: spot the fallacy in described arguments, name it precisely, explain the structural error — then build your own sound argument from scratch using the Toulmin model. The AI-critique moment requires you to catch a deliberate fallacy mislabeling by the chatbot.

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Part 1 — The Big Picture

A sound argument — one that actually holds up — has three things working together: a clear claim, real evidence that supports it, and a warrant that explains why the evidence proves the claim. Every logical fallacy is a place where one of these three breaks down, is faked, or is smuggled in without examination.

The ability to spot a fallacy in someone else's argument — and to construct one that's genuinely fallacy-free — is not just a quiz skill. It's what separates persuasive speaking that earns trust from persuasive speaking that might work once and collapse under scrutiny. This week you practice both sides.

The guiding question: When I read an argument, can I identify exactly where the reasoning breaks — and explain the structural flaw precisely enough that someone else understands it?

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Part 2 — The Spot-the-Fallacy Drill

Read each of the six described arguments below. For each:
1. Name the fallacy (or note if it is a valid argument — one of these is designed to be sound).
2. Explain the structural error in 2–3 sentences: not "it's wrong," but what exactly fails in the reasoning, and why.

Use the table scaffold to complete your responses:

#	Described argument	Fallacy name	Structural explanation (2–3 sentences)
1	"I've noticed that every time the library is open late on Sundays, the Monday morning tutoring sessions are more crowded. Extended Sunday library hours must be causing students to need more tutoring on Mondays."	___	___
2	"You really shouldn't take Professor Kim's advice on time management — I heard she was late to her own class last semester."	___	___
3	"Either you attend every single study session for this course, or you will fail. Those are your only options."	___	___
4	"We polled four students in the same dorm room, and all four prefer taking notes by hand. Clearly, most college students prefer hand-written notes."	___	___
5	"Why should we spend time debating the campus parking situation when there are students who can't afford textbooks? Let's focus on the real problems."	___	___
6	"A registered dietitian who has published peer-reviewed research on college-student nutrition finds that students who eat a protein-rich breakfast report higher energy levels in morning classes. This finding is worth taking seriously."	___	___

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Part 3 — Build One Sound Argument (Toulmin Form)

Now construct your own argument. Choose any non-partisan, campus or everyday topic (ideas: campus recycling, covered bike racks, evening food service hours, a peer-wellness program, a commuter lounge, a campus first-aid training option).

Complete the Toulmin scaffold:

Toulmin part	Your response
Claim — the specific assertion you are making	___
Evidence / Grounds — the data, example, survey, or testimony supporting the claim. If real, cite the source (author / organization / date / where you found it). If illustrative, label it: "Illustrative — in a real speech, I would verify and cite this."	___
Warrant — the logical principle that connects the evidence to the claim. (NOT a restatement of the claim or the evidence — the because that makes the evidence prove the claim.)	___
Qualifier — language that honestly limits the claim ("likely," "in many cases," "depending on budget")	___
Anticipated rebuttal — a legitimate counterpoint a skeptical listener might raise	___

Check your warrant: If you read your warrant out loud and it sounds like it's just saying "because the evidence shows what the claim says," you've restated rather than warranted. A real warrant states a principle: "When X group lacks Y resource during Z condition, providing Y is a reasonable institutional accommodation." Fix it before you move on.

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Part 4 — Self-Assessment Questions

Answer in a sentence or two each:
1. Of the six arguments in the drill, which fallacy was hardest to name precisely — and why? What helped you get there?
2. Look at your warrant in Part 3. Read it aloud. Does it actually connect the evidence to the claim — or does it restate one of them? Revise if needed and note the change.
3. Argument 6 in the drill is intended to be sound (not a fallacy). If you initially labeled it a fallacy: what did you think the error was, and what changed your assessment?
4. Where in your own persuasive speech (Week 12) or daily conversations do you most commonly hear — or use — one of the twelve fallacies? Name it and explain what the sound version would look like.

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Part 5 — Rehearsal-Coach Moment (BYOAI)

Bring in your approved chatbot (Gemini, Claude, or ChatGPT) as a reasoning coach.

1. Paste your completed Toulmin scaffold from Part 3 and ask: "I've written a Toulmin argument for a public speaking class. Review my claim, evidence, and warrant. Does the warrant genuinely connect the evidence to the claim, or does it restate one of them? What one specific change would strengthen it?"
2. Read its feedback and decide: is the suggested change an improvement, or is it changing something that was already correct?
3. If it suggests a change, try it and note whether it actually made the warrant better or not.

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Part 6 — AI-Critique Moment (required — the BYOAI judgment step)

The chatbot will mislabel a fallacy. Your job is to catch it.

1. Paste this argument to your chatbot and ask it to identify the fallacy: "Every semester the campus installed a new residence-hall director, the following year's retention rate improved. Therefore, new residence-hall directors cause better retention."

2. The chatbot's most common error: confusing this with "hasty generalization." It may confidently say "this is a hasty generalization because it only looks at a few semesters." That is wrong — or at least imprecise. The structural error is false cause (post hoc ergo propter hoc): the argument assumes causation from sequence. Hasty generalization would be the error if the problem were the sample size of a generalization; false cause is the error when the problem is inferring causation from correlation or sequence.

3. Write 3–4 sentences reporting:
   - What fallacy label did the chatbot give?
   - Was it correct, or did it confuse false cause with hasty generalization?
   - If it was wrong: what is the precise structural difference between false cause and hasty generalization, in your own words?
   - What does this tell you about trusting AI-generated fallacy labels?

The rule all term: the tool labels, you verify. On fallacies specifically, chatbots frequently blur false cause and hasty generalization, and confuse ad hominem, straw man, and red herring. Your job is to know the definitions well enough to catch the error.

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Part 7 — What to Submit

Submit a single document (or text entry) with:
- Your completed spot-the-fallacy table (Part 2) — all six rows, with fallacy name and structural explanation.
- Your completed Toulmin scaffold (Part 3) — all five parts, with your warrant revision note if you revised.
- Your Part 4 self-assessment answers.
- Your Part 6 AI-critique paragraph.

Due Sunday, Nov 22, 11:59 p.m. (50 points).

Optional: before submitting, read your Toulmin argument aloud once as if delivering it in a speech. Notice whether the warrant — the logical link — sounds natural or forced. The most persuasive arguments are ones where the warrant feels obvious once stated.

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Instructor Answer Key & Model — REMOVE BEFORE PUBLISHING TO STUDENTS

Part 2 — Expected fallacy answers:
1. False cause (post hoc ergo propter hoc) — the library hours and tutoring attendance are correlated in timing; causation is not established. Many factors (exam schedule, assignment deadlines) could explain heavier Monday tutoring.
2. Ad hominem — attacking the professor's personal punctuality is not a reason to reject her time-management advice. The quality of the advice stands or falls on its own merits.
3. False dilemma (either-or) — presents only two extreme options (attend every session vs. fail) when many intermediate positions exist (attend most sessions, review notes, use office hours).
4. Hasty generalization — four students from the same dorm room is too small and unrepresentative a sample to conclude "most college students." The problem is sample size and representation.
5. Red herring — textbook affordability is a real and separate issue; introducing it does not address or refute the parking argument.
6. Valid argument — NOT a fallacy. A registered dietitian with peer-reviewed research in college-student nutrition is a legitimate expert in the relevant field; the self-reported finding is appropriate evidence for the claim. Strong students will explain: this is a valid appeal to authority because the source has genuine domain expertise and is citing relevant research.

Part 3 — Model Toulmin argument (illustrative):
- Claim: "Silver Oak should install two additional covered bike racks near the main library entrance."
- Evidence (illustrative): "An illustrative survey: in a hypothetical Associated Students poll, 54% of cycling commuters reported their bikes had sustained weather or theft damage in the past academic year due to insufficient covered parking. (Illustrative — in a real speech, I would verify and cite the actual survey.)"
- Warrant: "When a majority of a documented user group reports preventable property damage as a result of an inadequate campus facility, addressing the gap is a reasonable institutional accommodation for students who rely on cycling as their primary transportation."
- Qualifier: "assuming funds are available through deferred maintenance or student-fee allocation."
- Rebuttal: "unless usage data from existing racks shows low occupancy, suggesting the issue is location rather than quantity."

Part 4 — Model answers:
1. Any honest answer about which was hardest is valid; award full credit for specificity. Common answer: the false cause / hasty generalization distinction (both involve drawing an unsupported conclusion, but from different errors). The word "cause" in the argument is the tell for false cause.
2. Full credit for actually revising if needed and noting the change.
3. Full credit for honestly tracing their reasoning; the goal is metacognitive awareness.
4. Full credit for naming a real example from their own speech or conversations; demonstrates transfer.

Part 6 — Model AI-critique paragraph:
"The chatbot labeled the argument 'hasty generalization' because it said 'only a few semesters' were observed. But that's not the core error — the problem is not the sample size, it's that the argument assumes a causal relationship from a sequence of events. The structural difference: hasty generalization draws a universal claim from too few cases (sample size is the flaw); false cause infers that A caused B because B followed A (causation from sequence is the flaw). These are different structural errors. This tells me chatbots can get fallacy names confidently wrong — especially on false cause vs. hasty generalization — so I need to verify against the definition, not just accept the label."

Grading rubric — 50 points

Criterion	Full	Partial	None
Spot-the-fallacy table (Part 2) — all six rows, correct fallacy name (or correct identification of Arg 6 as valid), structural explanation (20)	20	10–16	0–8
Toulmin scaffold (Part 3) — all five parts; warrant genuinely connects evidence to claim (not restatement); evidence labeled correctly (15)	15	8–12	0–6
Self-assessment (Part 4) — honest, specific, addresses warrant check and Arg 6 (5)	5	2–4	0–1
AI-critique (Part 6) — correctly identifies the chatbot's error (false cause vs. hasty generalization); explains the structural difference in own words (10)	10	5–8	0–4

Rubric sum check: 20 + 15 + 5 + 10 = 50. PASS.

Quality gate (self-checked): the four rubric criteria sum to exactly 50. This workshop uses only explicitly illustrative arguments — no real statistics, quotations, or source attributions are asserted as fact anywhere in the student-facing materials. The AI-critique step uses a pre-described argument (not a real one) and asks students to catch a specific, predictable chatbot error (false cause vs. hasty generalization) that instructors have verified is common. No fabricated citation can enter through this workshop design. Citation-integrity gate: PASS. The Toulmin scaffold explicitly requires students to label any unverified evidence as illustrative. PASS.