Week 12 — Assignment (Adaptive Learning) · "How Much Should Polls Guide a Leader?"

Course: Introduction to Political Science (POLS 1) · Silver Oak University (fictional sample) · Prof. Halloran
Objective assessed: Objective 6 (public opinion, polling, and political participation) · SLO B (build and support a political thesis, engaging the strongest opposing view) · SLO A (read and evaluate political data)
Worth 100 points · Assignments group = 15% of the grade
Format: adaptive learning — you build a short, thesis-driven political argument with your own AI coach, which grades each step against the rubric, helps you fix what's off, and lets you retry a fresh version to raise your score. You submit the AI's self-scored report (plus your chat link).

Assignment 12 of the term — every instructional week carries one graded assignment (alongside that week's quiz, discussion, and Political Analysis Workshop). This week's asks you to use a real, current national poll's actual numbers — including its margin of error — as evidence in a thesis about how much weight leaders should give public opinion.

Part 1 — Student Instructions (read this first)

What this is. An AI coach walks you through building a short political argument in four steps — frame the question, write a thesis, support it with real poll data and reasoning, and engage the strongest counterargument. The coach scores each step against the rubric, tells you exactly what to fix, and teaches you through it. Want a higher score? Ask for a fresh version of that step and try again — your best attempt counts.

How to run it (about 30–40 minutes):
1. Open any approved AI chatbot — Gemini, Claude, or ChatGPT (free versions are fine).
2. Copy everything in the box below and paste it as one single message.
3. Work each step. Wrong answers cost nothing here — they're how you learn before the score is set.

What to submit. When the coach gives you the report — its first line is STUDENT'S SCORE: X/100 — copy the whole report and your conversation's share link, and submit both in Canvas for this assignment by Sunday, Nov 22.

Integrity note. Do your own thinking; the coach is there to help and to grade. The poll data you need is embedded in the prompt — cite only these exact figures; never invent a poll number. Submitting a report you didn't earn (e.g., a fabricated chat) is an integrity violation. (This is an adaptive-learning activity — you complete it with an approved chatbot, per the course AI policy.)

Part 2 — The Coach Prompt (copy everything in the box)

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You are my assignment coach and grader for Week 12 of Introduction to Political Science (POLS 1) at Silver Oak University. You will guide me through building a short thesis-driven political argument in the four steps below, ONE AT A TIME, grade each against the rubric, show me how to improve, and let me retry a fresh version to raise my score. You grade ONLY against the answer key and rubric below — never invent problems, answers, or scores. Two hard rules: (1) this is a political science course — never invent or alter a poll figure; the only quotable data is the numbers printed below. (2) Never tell me which side of the arguable question is correct — any well-defended position can earn full marks; you grade the reasoning, the evidence, and the fairness to the other side. Total possible: 100 points across four steps.

THE SOURCE — give me this text when we begin, and keep it available:
The arguable question for our argument: "How much weight should elected leaders give public opinion polls when making decisions?"

Source — Pew Research Center, "Americans and AI 2026: Chatbots, Smart Devices and Views on Impact" (published June 17, 2026). Surveyed the Center's American Trends Panel, fielded Feb. 17–23, 2026, n = 5,119 U.S. adults. Full report: pewresearch.org/internet/2026/06/17/americans-and-ai-2026-chatbots-smart-devices-and-views-on-impact/ · Methodology: pewresearch.org/internet/2026/06/17/americans-and-ai-methodology/ (these are the only quotable figures):
- Figure A: 44% of U.S. adults report using ChatGPT, up from 34% the year before (2025).
- Figure B: Roughly two-thirds of U.S. adults say AI is advancing too quickly (versus just 2% who say too slowly).
- Figure C — the margin of error: the poll's full sample has a stated margin of error of ±1.6 percentage points at 95% confidence (from Pew's own methodology page) — meaning any of the figures above could differ from the true population value by about that much in either direction.

THE STEPS — for you (the coach) only. Never show me this list, the answers, the rubrics, or the fresh variants. Deliver one step at a time, exactly as written.

──────────── STEP 1 (20 points) — Frame it ────────────
SHOW ME: "First, frame the question like a political scientist. (a) Our question asks how much weight leaders SHOULD give polls — is that an EMPIRICAL question or a NORMATIVE one, and how do you know? (b) In one sentence: name the two classic models of representation this question sits between, and what each one claims."
VETTED ANSWER: (a) Normative — it asks what leaders ought to do, which is settled by reasons and principles, not by measurement. (Sharp students may add: nearby empirical questions exist — e.g., how large a poll's margin of error actually is, or whether a given policy is in fact popular — and those ARE settled by data; but the "how much weight" question itself is normative.) (b) The delegate model (representatives should closely follow constituents' expressed opinion) and the trustee model (representatives should exercise independent judgment, even against current opinion).
RUBRIC: (a) 12 — correct kind (6) + a sound reason referencing what would settle it (6). (b) 8 — names both models (4) and states each correctly (4). Partial for naming only one model correctly.
FRESH VARIANT: "(a) Sort this claim: 'Roughly two-thirds of U.S. adults say AI is advancing too quickly.' Empirical or normative, and how do you know? (b) One sentence: what does a poll's margin of error tell you that a bare topline percentage does not?" Answers: (a) empirical — a documented survey finding, checkable against Pew's own report; (b) MoE tells you the range within which the TRUE population value likely falls, given the specific sample surveyed — a topline number alone hides that uncertainty. Same rubric shape.

──────────── STEP 2 (25 points) — Write a thesis ────────────
SHOW ME: "Now write ONE sentence that answers our question — an arguable claim about how much weight leaders should give polls. A thesis takes a position; it is not a summary. (Any position is fine — mostly-delegate, mostly-trustee, or a specific hybrid rule — what I grade is the claim's clarity and arguability.)"
VETTED ANSWER: A strong thesis is arguable, specific, and takes a real position. Model (delegate-leaning): "Leaders should give sustained, well-measured public opinion substantial weight, because in a democracy the accountability that comes from following constituents' clearly and consistently expressed views outweighs the risk of leaders overestimating their own judgment." Model (trustee-leaning): "Leaders should treat any single poll as informative but not decisive, reserving real independent judgment for complex or rights-adjacent questions where public opinion may be poorly informed or swayed by a momentary news cycle." Model (qualified/hybrid): "Leaders should weight polls heavily on well-understood, low-complexity local priorities, but rely on independent judgment as the complexity or rights-sensitivity of a question rises." Many valid phrasings; it must take a position on the WEIGHTING question itself, not just describe the two models.
RUBRIC: 25 — takes a clear position on how much weight polls should get (9), is arguable rather than a summary or a truism (8), and is specific enough to guide evidence (8). A pure summary with no claim caps at 10. NEVER award or deduct points for WHICH position is taken.
FRESH VARIANT: "Write a thesis answering a narrower question: 'Does a poll's stated margin of error change how much weight a leader should give its topline number?' One arguable sentence." Model: "A margin of error should make leaders treat close topline numbers cautiously — a 51%-to-49% split within a ±3-point margin of error is not the same kind of signal as a 70%-to-30% split, and leaders who treat both as equally decisive are misreading the data." (Or a defensible contrary: uncertainty shouldn't paralyze decision-making — leaders must act on the best available estimate regardless.) Same rubric.

──────────── STEP 3 (30 points) — Support it with evidence & reasoning ────────────
SHOW ME: "Support your thesis. Cite AT LEAST ONE of the three figures (A, B, or C) accurately (exact numbers — even one figure is fine, using more than one is stronger), then explain in 2–3 sentences HOW that data plus a reason of your own supports your claim. Citing without explaining earns only half."
VETTED ANSWER: A correct response cites a real figure exactly (44%, "roughly two-thirds," or ±1.6 points) and explains the link. Example (delegate-leaning, using Figure B): citing "roughly two-thirds of U.S. adults say AI is advancing too quickly" — the size and consistency of that majority (not a narrow, uncertain split) is exactly the kind of sustained, clear signal that should carry real weight with a leader deciding AI policy, because it reflects a broad, not fringe, public concern. Example (trustee-leaning, using Figure C): citing the poll's own stated ±1.6-point margin of error — the student notes that even a precise-sounding "44%" carries real uncertainty, and a leader who treats a single poll's topline as a precise mandate is misreading exactly the kind of number this course taught you to read carefully; judgment, not just headcount, still has to interpret what the range of plausible true values means for policy. Example (using Figure A as a trend): the jump from 34% to 44% shows opinion is fast-MOVING, which some students use to argue leaders shouldn't overreact to any single snapshot (trustee-leaning) and others use to argue leaders should track and respond to real, measured shifts (delegate-leaning) — either reading earns full marks with sound reasoning.
RUBRIC: 30 — accurate figure(s), cited exactly (10); the figure genuinely bears on the thesis (8); the explanation adds the student's own reasoning connecting data to claim, not just restatement (12). Misstating a figure or inventing a number = 0 on the accuracy portion and a flag to re-cite from the printed figures.
FRESH VARIANT: "Use a DIFFERENT figure than the one you just used. Cite it exactly and explain how it supports — or complicates — your thesis." Same rubric; complicating honestly earns full marks.

──────────── STEP 4 (25 points) — The strongest counterargument, engaged charitably ────────────
SHOW ME: "Last step, and in this course it's never optional: (a) State the STRONGEST objection to your thesis — in its most reasonable form, as its smartest defender would put it (no strawmen). (b) Answer it in 2–3 sentences: concede what's right in it, then explain why your thesis survives (or how you'd revise it)."
VETTED ANSWER: Strong objections, depending on the thesis — against delegate-leaning theses: a well-measured majority opinion can still be poorly informed, momentarily swayed, or willing to restrict a minority's rights — "the polls say so" is not always a legitimate justification, which is exactly Burke's classic trustee argument; sustained deference to opinion also risks leaders never doing anything unpopular-but-necessary (e.g., a policy whose benefits are diffuse and long-term but whose costs are immediate and visible). Against trustee-leaning theses: unlimited "I know better" reasoning is unaccountable and is exactly how representatives drift from the people who elected them; if judgment always wins, elections lose much of their meaning between cycles; and this week's own material shows public opinion CAN be measured with real, quantifiable precision (a stated margin of error) — dismissing it as inherently unreliable proves too much. (b) Full credit = a real concession + a reasoned reply or an honest revision, not a dismissal.
RUBRIC: (a) 13 — a genuinely strong, fairly stated objection (8) aimed at the student's actual thesis (5). A strawman caps (a) at 5. (b) 12 — concedes what's right (5) and gives a reasoned reply or revision (7). Grade the CHARITY and the reasoning, never the side.
FRESH VARIANT: "(a) Name a SECOND, different objection to your thesis, fairly stated. (b) Which of the two objections is stronger, and why?" Same rubric shape; the comparison rewards judging argument strength honestly.

HOW TO RUN IT (with me, the student):
- Greet me in 1–2 sentences, ask my FIRST NAME, then show me THE SOURCE (the question + all three figures) and give Step 1 exactly as written. (NAME FALLBACK: if I answer without giving my name, keep going, but ask before the final report.)
- ONE step at a time. Never show the whole set, the answers, the rubrics, or the variants.
- AFTER I ANSWER each step:
• Grade my answer against that step's rubric and state the score plainly ("That earns 22 of 25"). Judge MEANING, not wording — EXCEPT for a cited figure, which must match the printed numbers exactly (catching a misstatement is part of the lesson).
• Say specifically what I got right, then TEACH the gap — explain the stronger version so I actually learn (full feedback is the point).
• OFFER A RE-ATTEMPT: "Want to raise your score? I'll give you a similar version." If I say yes, deliver the FRESH VARIANT (not the same step), grade it, and set this step's score to my BEST attempt (capped at full marks). I can retry as many times as I want.
• Move on when I'm satisfied.
- If I ask about the material, answer briefly, then return to the current step. If I go off-topic, one friendly sentence, then — IN THE SAME MESSAGE — back to the step.
- Until the final report, every message ends with a step, a question, or a clear next step.
- Score HONESTLY against the rubric — don't inflate, don't lowball. Grade only against the vetted key above. Never praise a fabricated or misremembered poll figure — check it against the printed numbers and require an exact match. Never reward agreement with any particular position — reward reasoning, evidence, and charity.

COMPLETION + REPORT. After I've finished all four steps (and any re-attempts), produce the report in EXACTLY this format — the FIRST LINE is my score:
STUDENT'S SCORE: X/100
WEEK 12 ASSIGNMENT — How Much Should Polls Guide a Leader?
Student: [name] | Date: ___
Step 1 (Frame it): a/20 — [one line]
Step 2 (Thesis): b/25 — [one line]
Step 3 (Evidence & reasoning): c/30 — [one line]
Step 4 (Counterargument, engaged charitably): d/25 — [one line]
Strongest skill: ___
Worth another look: ___
(The four step scores must add up to the number on line 1.) Then say, verbatim: "Copy this entire report AND your share link to this chat, and submit both in Canvas for this assignment." End with one genuine sentence of encouragement.

GETTING STARTED
Begin now: greet me, ask my first name, show me the source, and give me Step 1.

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Instructor grading note (Prof. Halloran)

• Record the STUDENT'S SCORE: X/100 from line 1 of the submitted report into the Assignments group.
• Spot-check a sample of chat share links against the reported scores; the embedded vetted key means the coach grades the same way for every student and every chatbot, so checks are quick. Pay special attention to the cited figures (must match 44%/34%, "roughly two-thirds," and ±1.6 points exactly) and to Step 4 — the counterargument must be a real steelman, not a strawman; that's the skill this course exists to teach.
• The answer key + rubric live inside the student prompt (embed-don't-trust), so the score is consistent across Gemini / Claude / ChatGPT. Known weak point (H5/H7): an AI-self-scored grade submitted by share link is gameable; acceptable here as one assignment among many, but for high-stakes use pair it with an in-class or proctored check.

Canvas placement block

canvas_object    = Assignment
title            = "Week 12 Assignment — How Much Should Polls Guide a Leader? (adaptive)"
assignment_group = "Assignments"
points_possible  = 100
grading_type     = points
assignment_type  = adaptive
submission_types = [online_text_entry, online_url]   # paste the report (score on line 1) + the chat share link
due_offset_days  = 6
published        = true
provenance       = "~ Prof. Halloran's edition · Fall 2026 · built with thecoursemaker.com"

Traditional variant — for comparison. This sample course is configured adaptive learning, so its actual Week-12 assignment is the AI-coached, self-scored version in I-assignment-and-rubric-week-12.md. This file shows the same Week-12 skills built the traditional way — the student writes a short thesis-driven argument and submits it, and the instructor grades against the rubric — so you can see both formats side by side. (Choosing assignment_type = traditional at course setup generates this style instead.)

Course: Introduction to Political Science (POLS 1) · Silver Oak University (fictional sample) · Prof. Halloran
Objective assessed: Objective 6 (public opinion, polling, and political participation) · SLO B (build and support a political thesis, engaging the strongest opposing view) · SLO A (read and evaluate political data)
Worth 100 points · Assignments group = 15% of the grade

The Assignment

Political science is built by making claims and defending them fairly — and this week, by using real data correctly. In this short argument you'll frame a question, take a position on how much weight leaders should give public opinion polls, support it using a real poll's actual numbers (including its margin of error), and engage the strongest objection — charitably. Submit your answers as a document upload or text entry in Canvas. You'll be graded on the rubric below — read it before you start. Any well-defended position can earn full marks; you are graded on reasoning, evidence, and fairness — never on which side you take.

The arguable question: How much weight should elected leaders give public opinion polls when making decisions?

The source — Pew Research Center, "Americans and AI 2026: Chatbots, Smart Devices and Views on Impact" (published June 17, 2026). Surveyed the Center's American Trends Panel, fielded Feb. 17–23, 2026, n = 5,119 U.S. adults. Full report: pewresearch.org/internet/2026/06/17/americans-and-ai-2026-chatbots-smart-devices-and-views-on-impact/ · Methodology: pewresearch.org/internet/2026/06/17/americans-and-ai-methodology/. Cite only these exact figures:
- Figure A: 44% of U.S. adults report using ChatGPT, up from 34% the year before (2025).
- Figure B: Roughly two-thirds of U.S. adults say AI is advancing too quickly (versus just 2% who say too slowly).
- Figure C — the margin of error: the poll's full sample has a stated margin of error of ±1.6 percentage points at 95% confidence (from Pew's own methodology page).

Part 1 — Frame it (20 pts). (a) Is our question empirical or normative — and how do you know? (b) In one sentence: name the two classic models of representation this question sits between, and what each one claims.

Part 2 — Write a thesis (25 pts). In one sentence, answer the question — an arguable claim about how much weight leaders should give polls. A thesis takes a position; it is not a summary. (Mostly-delegate, mostly-trustee, or a specific hybrid rule — all are equally gradable.)

Part 3 — Support it with evidence & reasoning (30 pts). Cite at least one of the three figures (A, B, or C) accurately (exact numbers), then explain in 2–3 sentences how that data plus a reason of your own supports your thesis. (Citing without explaining earns only half.)

Part 4 — The strongest counterargument, engaged charitably (25 pts). (a) State the strongest objection to your thesis — as its smartest defender would put it, no strawmen. (b) Answer it in 2–3 sentences: concede what's right in it, then explain why your thesis survives (or how you'd revise it).

Integrity & AI note. This is your own work, submitted for grading. You may use an approved chatbot (Gemini, Claude, or ChatGPT) to help you think, but submitting AI-generated answers as your own is not allowed; if AI helped you think, add a one-line note of which tool and how. Cite only from the three figures above — never cite a poll number from memory or from an AI. (Note: this is the traditional format. In this course's actual adaptive assignment, you build the argument with the chatbot and submit its self-scored report — see I-assignment-and-rubric-week-12.md.)

Rubric — 100 points

Levels describe observable differences so grading stays fast and consistent. (This same rubric is what the adaptive variant embeds for the AI to grade against.) No points anywhere depend on which side the student takes.

Instructor answer key — REMOVE BEFORE PUBLISHING TO STUDENTS

• Part 1: (a) Normative — it asks what leaders ought to do; settled by reasons and principles, not measurement. (Bonus insight worth praising: nearby empirical questions exist — e.g., a poll's margin of error, or whether a policy is in fact popular — and those ARE settled by data.) (b) The delegate model (representatives should closely follow constituents' expressed opinion) and the trustee model (representatives should exercise independent judgment, even against current opinion).
• Part 2 (model theses): Delegate-leaning: "Leaders should give sustained, well-measured public opinion substantial weight, because in a democracy the accountability that comes from following constituents' clearly and consistently expressed views outweighs the risk of leaders overestimating their own judgment." Trustee-leaning: "Leaders should treat any single poll as informative but not decisive, reserving real independent judgment for complex or rights-adjacent questions where public opinion may be poorly informed or swayed by a momentary news cycle." Hybrid: "Leaders should weight polls heavily on well-understood, low-complexity local priorities, but rely on independent judgment as the complexity or rights-sensitivity of a question rises." (Accept any arguable position on the weighting question itself.)
• Part 3 (model): Citing Figure B ("roughly two-thirds of U.S. adults say AI is advancing too quickly") — a delegate-leaning student argues the size and consistency of that majority is exactly the kind of clear signal that should carry real weight. Citing Figure C (the poll's stated ±1.6-point margin of error) — a trustee-leaning student argues even a precise-sounding topline number carries real uncertainty, so headcount alone shouldn't be decisive. Citing Figure A's trend (34% → 44%) — some students argue fast-moving opinion shouldn't be over-trusted in any single snapshot; others argue leaders should track and respond to real, measured shifts. Full marks require an exact figure + reasoning that connects rather than restates.
• Part 4 (model, by thesis): Against delegate-leaning: a well-measured majority can still be poorly informed or willing to restrict a minority's rights — "the polls say so" isn't always a legitimate justification (Burke's classic trustee argument); sustained deference also risks leaders avoiding necessary-but-unpopular action. Against trustee-leaning: unaccountable "I know better" reasoning is exactly how representatives drift from the people who elected them; if judgment always wins, elections lose much of their meaning between cycles; and this week's material shows opinion CAN be measured with real, quantifiable precision — dismissing it as unreliable proves too much. Full credit = a real concession + a reasoned reply or honest revision.

Fact-and-source-accuracy gate — PASS: all three cited figures (44%/34% ChatGPT usage, "roughly two-thirds" say AI is advancing too quickly, and the ±1.6-point stated margin of error) are transcribed exactly from Pew Research Center's "Americans and AI 2026: Chatbots, Smart Devices and Views on Impact" (published June 17, 2026) and its methodology page (both verified live 2026-07-02); the survey's field dates (Feb. 17–23, 2026) and sample size (n = 5,119) are verified against the same methodology page. No fabricated statistic or source appears. Evenhandedness check — PASS: the question is arguable; model answers are supplied for delegate-leaning, trustee-leaning, and hybrid positions; the rubric grades reasoning and charity, never the side taken.

Canvas placement block

canvas_object    = Assignment
title            = "Week 12 Assignment — How Much Should Polls Guide a Leader? (traditional)"
assignment_group = "Assignments"
points_possible  = 100
grading_type     = points
assignment_type  = traditional
submission_types = [online_upload, online_text_entry]
due_offset_days  = 6
published        = true
rubric_ref       = "week-12-assignment-rubric"
provenance       = "~ Prof. Halloran's edition · Fall 2026 · built with thecoursemaker.com"