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Evaluate 'AI' Product Claims with Data: A Teacher's Mini-Project from CES Demos

UUnknown

2026-02-16

9 min read

Turn CES AI hype into a classroom mini-project: design experiments, collect data, and use hypothesis testing to evaluate gadget claims.

Hook: Turn CES Demos Hype into Classroom Data—Fast

Students and teachers are overwhelmed by flashy CES demos that claim everything from “99% accurate” detection to “fully personalized” experiences. How do you teach critical thinking and statistics when marketing blurs with measurement? This mini-project gives you a classroom-ready, evidence-first pathway: design simple experiments, collect reproducible data, and use hypothesis testing to evaluate real AI gadget claims.

Why this matters in 2026

CES 2026 showed the world what education already suspected: a tidal wave of products labeled AI—many solving real problems, many serving as marketing garnish. Late 2025 and early 2026 brought sharper regulatory pressure (EU AI Act rollouts and stronger FTC scrutiny in the U.S.), new model-card disclosure norms, and a push for measurement transparency. That means students can and should treat AI product claims as testable scientific statements. This mini-project teaches critical data literacy while aligning with current trends in AI governance and consumer protection.

“Too often, AI isn't solving a real problem. It's simply a marketing strategy.”

Project Overview — The Teacher’s One-Page Plan

Timeframe: 1–2 weeks (3–6 class sessions) or a short-term lab sequence. Group size: pairs or trios. Learning goals:

Experimental design: independent/dependent variables, controls, randomization
Hypothesis testing: null vs alternative, significance, p-values, effect sizes
Data collection & ethics: measurement reliability, consent, privacy
Communication: visualizations, conclusions tied to evidence

Step-by-step Mini-Project (Classroom-Ready)

1. Pick a claim you can test

Choose a short, falsifiable claim from an AI gadget demo. Focus on measurable outcomes. Examples tailored to classroom settings:

“This AI toothbrush removes 90% of plaque in one cleaning.”
“The sleep mask improves sleep efficiency by 20%.”
“The smart feeder dispenses the correct portion 99% of the time.”
“The personalization feature recommends a relevant snack 4 out of 5 times.”

2. Turn the claim into a hypothesis

Write a null hypothesis (H0) that reflects the claim and an alternative hypothesis (Ha) that says the claim is false or different. Be explicit about the direction.

Example (proportion): H0: p = 0.99 (feeder correct 99%); Ha: p < 0.99.
Example (paired mean): H0: mean increase = 20% sleep efficiency; Ha: mean increase < 20%.

3. Define variables and measurements

Operational definitions are everything. Decide exactly how you measure outcomes and on what scale. Use objective instruments when possible (timers, smartphone sensors, scoring rubrics).

Accuracy/proportion: correct vs incorrect events.
Performance/continuous: pre/post scores (sleep efficiency %, plaque index 0–5).
Personalization/categorical: 3-level relevance score (0 = not relevant, 1 = somewhat, 2 = relevant).

4. Design the experiment

Key design elements to teach and implement:

Sample size: small classroom samples (n=10–30) are fine for teaching; explain power concepts and that larger n improves confidence.
Randomization: random order of trials or randomized assignment to control/device conditions.
Controls: a baseline measurement (no AI) or a gold-standard comparator where possible.
Blinding: single-blind (students don't know device mode) reduces bias in subjective ratings.

5. Collect data with a protocol

Create a short, replicable protocol sheet and a data collection table. Require timestamps, operator initials, and instrument IDs. Emphasize repetition to capture variability.

6. Analyze with the right test

Match test to data type. Here are classroom-friendly examples with calculation steps.

Example A — One-sample proportion test (feeder accuracy)

Claim: p0 = 0.99. Students run 100 feedings; observed correct = 94.

Observed proportion p̂ = 94/100 = 0.94.
Standard error SE = sqrt(p0*(1-p0)/n) = sqrt(0.99*0.01/100) ≈ 0.00995.
z = (p̂ - p0)/SE = (0.94 - 0.99)/0.00995 ≈ -5.03.
p-value ≪ 0.001 → reject H0. Evidence suggests accuracy < 99%.

Example B — Paired t-test (sleep mask)

Claim: sleep efficiency improves by 20% with device. Students measure baseline and with-device efficiency for 12 volunteers. Suppose differences (device - baseline) have mean d̄ = 3.5% and SD sd = 2.8%.

Standard error SE = sd / sqrt(n) = 2.8 / sqrt(12) ≈ 0.808.
t = d̄ / SE = 3.5 / 0.808 ≈ 4.33.
Degrees of freedom = n-1 = 11. Critical t (α=0.05, two-sided) ≈ 2.201. t > 2.201 → reject H0.
Conclusion: improvement is significant, but mean increase (3.5%) is far below the claimed 20%—teach students to look at effect size, not just significance.

7. Interpret results — accuracy vs practical significance

Distinguish statistical significance from practical importance. A tiny effect can be statistically significant with low variance; a large claimed improvement that is non-significant may need more data. Ask: does the observed effect change user behavior or safety?

Data Collection Templates (Copy-paste for class)

Provide students with a simple table to standardize collection. Teachers can print or share digitally.

Sample Data Sheet — One-Sample Proportion
Trial	Timestamp	Outcome (1=correct, 0=incorrect)	Notes
1	2026-01-08 10:02	1	Normal disp.
2	2026-01-08 10:07	0	Under-dispense
3	…	…	…

Sample Data Sheet — Paired Measurements
Subject	Baseline (%)	Device (%)	Difference	Notes
1	68	72	4	Good sleep
2	74	78	4	Restless
…	…	…	…	…

Classroom Worksheets & Practice Problems

Use these short exercises as warm-ups or homework. Each emphasizes one statistical concept.

Proportion warm-up: An AI pen claims 95% handwriting recognition for math formulas. In 50 student trials, it correctly transcribed 43. Test H0: p = 0.95 at α=0.05.
Paired warm-up: Students measure time to solve a geometry problem with and without a problem-solver app. n=10 pairs; compute paired t-test and report effect size.
Chi-square intro: A personalization feature recommends content across 3 categories. Compare observed distribution to expected 50/30/20 split.

Answer keys — brief

Problem 1: p̂ = 43/50 = 0.86. SE = sqrt(0.95*0.05/50)=0.0308, z = (0.86-0.95)/0.0308 ≈ -2.92 < -1.96 → reject H0.
Problem 2: Steps: compute differences, mean diff, sd, SE, t. Interpret p-value vs α.
Problem 3: Compute chi-square statistic Σ (O-E)^2/E and compare to df=2 critical value (≈5.99 at α=0.05).

Practical Classroom Tips

Keep trials short. Use n=10–30 to fit class time but discuss limitations of small n.
Pre-register the experiment: have students write a brief plan (claim, hypotheses, test, sample size rationale) before collecting data.
Use digital tools: Google Sheets for quick stats, or cloud notebooks (Binder, Colab) for Python/pandas exercises. Show students how to compute a t-test with built-in functions but make them do at least one manual calculation to internalize the math.
Embed reproducibility: require raw data files and a short method write-up so others can replicate the test.

Ethics, Privacy, and Safety — Non-Negotiables

AI gadgets often collect personal data. Teach students to consider:

Consent: always get explicit consent from participants (and guardians for minors).
Minimize data collection: collect only what’s necessary, anonymize identifiers.
Device safety: check devices for hazards before tests (electrical or mechanical).
Transparency: note any firmware/app settings that change results (models can update over time).

Assessment Rubric (Quick)

Assign a single grade with multiple criteria to reflect both statistics and critical thinking.

Design clarity (25%): hypothesis stated, variables defined, sample size justified.
Data quality (25%): organized, reproducible, ethics observed.
Analysis (25%): correct test selection, appropriate calculations, assumptions checked.
Interpretation & communication (25%): conclusions matched to evidence, limitations discussed, visuals clear.

Advanced Extensions for High School / AP / Intro Stats

Power analysis: introduce the idea that experiments can miss real effects if underpowered; use free calculators to explore sample-size trade-offs.
Model cards: ask students to read a product’s model card (if available) and critique training data and intended use.
Time-series analysis: for gadgets that log long-term behavior (sleep trackers), teach autocorrelation and moving averages—consider edge AI implications when working with streaming device data.
Machine learning fairness tests: evaluate personalization across demographic groups using stratified analysis and chi-square tests. For broader measurement strategies, see work on advanced measurement approaches.

Tools & Resources (2026-forward)

Keep the toolchain light for classroom use. Recent 2025–26 developments made some options more accessible:

Free Colab notebooks with pre-built stats cells (many educators published templates after CES 2026).
Updated APIs from wearables (2025 firmware) that allow export of sleep and activity metrics for research use.
Regulatory resources: summaries of the EU AI Act applicability and FTC guidelines on deceptive claims (use these when discussing business ethics).

Case Study: From CES Claim to Classroom Conclusion

A CES demo claimed a sleep mask “improves sleep efficiency by 20%.” A class of 12 students ran a paired design. Results: mean improvement = 3.5% (sd = 2.8%). The students’ t-test rejected the null of zero improvement but showed the observed effect was an order of magnitude smaller than the product claim. The class wrote a one-page conclusion: claim not supported, measured benefit small, recommend manufacturer provide raw validation data and model-card specs. This exercise was memorable because it taught both statistical reasoning and consumer skepticism.

Common Pitfalls & How to Avoid Them

Over-interpreting small n: teach confidence intervals and show wide intervals when data are scarce.
Mixing measurement error and bias: train students to calibrate instruments and log conditions (lighting, volume).
Forgetting multiple comparisons: if students test many claims, adjust significance thresholds or pre-register primary outcome.
Letting marketing drive the experiment: encourage independent replication and transparency.

Why This Mini-Project Works

It maps statistical concepts onto current, tangible technology students see in the news and at events like CES. The project’s short cycle time yields quick feedback, reinforces the scientific method, and teaches evidence-based skepticism—skills essential in 2026 when AI claims meet stronger regulation and consumers demand transparency.

Actionable Takeaways

Pick a single, measurable claim; write H0 and Ha before collecting data.
Keep trials reproducible: use clear protocols, timestamps, and anonymize participants.
Match the statistical test to your data type: proportion tests for accuracy, paired t-tests for before/after, chi-square for categorical distributions.
Report effect sizes and confidence intervals, not just p-values—teach the difference between statistical and practical significance.
Discuss ethics: consent, privacy, and potential device updates that alter results over time.

Ready-to-Use: One-Week Syllabus

Day 1 — Intro: choose claim, write hypotheses, plan protocol.
Day 2 — Pilot: run 5–10 pilot trials to refine measurement.
Day 3 — Data collection: complete full sample.
Day 4 — Analysis: compute stats, visualize, draft conclusions.
Day 5 — Presentations: 5-minute reports + class discussion on limitations and next steps.

Call to Action

Bring CES-style claims into your classroom this semester: pick one gadget, run a focused mini-project, and teach students how data—clear, transparent, repeatable—turns hype into verifiable facts. Want ready-to-print worksheets, a Google Sheets template, and a Colab notebook with built-in stats for students? Download our teacher pack (or request it from your department) and share your class findings in our educator forum—help build a repository of reproducible classroom tests for the next generation of critical thinkers.

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