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AI Talent Scout: Can the Machine Be Trusted?

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Grades 6-860-75 minSource: Creative Dad Company

Students form a review team for an AI system that sorts audition video clips for a talent agency. They build a simple rule-based system and a small trained AI model, then test both for accuracy and bias.

Budget levels

Amazon prices checked Oct 9-10, 2026 and may change

The brief lists no new materials.

Materials links go to Amazon. Prices are estimates and may change. As an Amazon Associate, Creative Dad Company earns from qualifying purchases.

Quick Start

Digital downloads only.

$0 to buy. Printing noted separately.

Student time: one class period, one day.

Teacher prep / build-ahead: separate from student time.

Scale it

The default plan is one class period on a single day.

Scale back: Fit it into a shorter period, use fewer effects, or run a demo instead of a build.

Scale up: Add one extra effect, or spread the share-out across another day.

Ambiance video

Ambiance video coming soon from Creative Dad Studio

  • No new materials (per brief)use what you have

Full Experience

Required materials for the class.

$0 per class (class of about 30, 6 groups).

Student time: one day.

Teacher prep / build-ahead: separate from student time.

Scale it

The default plan is one school day.

Scale back: Fit the build into one period with fewer effects.

Scale up: Spread the work across days, or add more effects.

Total Transformation

The bigger kit, with every optional item included.

$0 per class (class of about 30, 6 groups).

Student time: one day (one session, one-day event).

Teacher prep / build-ahead: separate from student time.

Scale it

The default plan is one session, a one-day event.

Scale back: Shorten the event, or run a demo for a smaller audience.

Scale up: Spread the build and the event across days, or invite a bigger audience.

Materials

Safe gestures or sounds made by students (training and test examples); Shared results chart (paper)

Materials links go to Amazon. Prices are estimates and may change.

As an Amazon Associate, Creative Dad Company earns from qualifying purchases.

  • Shared results chart

    Shared results chart (paper)

    No link

Steps

  1. Learn how a camera turns light into pixel brightness and a microphone turns sound waves into numbers, and why lighting or background noise can change what a system 'sees' or 'hears'.
  2. Write the design spec before building. Criteria: at least 80 percent correct, no more than 2 false alarms per 20 clips, and accuracy for any two groups of students within 10 percentage points of each other. Constraints: works under normal classroom lighting, under 30 training examples, safe gestures or sounds only, and a person makes the final decision. List impacts on applicants if the system is wrong. Also list at least two impacts on the natural environment, such as electricity used to train and run the AI and e-waste from extra cameras or devices, and add constraints for them: use only devices the program already owns and keep total training time under 10 minutes.
  3. Build a rule-based system with no AI to sort audition clips, then train a small AI model (such as Teachable Machine) on safe gestures or sounds made by students.
  4. Test both systems on 20 new examples each. Record correct, false alarms and misses on a shared chart.
  5. Change the lighting, background, voice or movement and test again. Record how the numbers change and which spec limits were broken.
  6. Score both systems in a decision matrix against every criterion and constraint in the spec, using the test data.
  7. Give a go, revise or reject recommendation backed by the matrix, and design a fair audition process where a person makes the final decision.

Standards mapped

  • NGSS · MS-ETS1-1

    Define the criteria and constraints of a design problem with sufficient precision to ensure a successful solution, taking into account relevant scientific principles and potential impacts on people and the natural environment that may limit possible solutions.

  • NGSS · MS-ETS1-2

    Evaluate competing design solutions using a systematic process to determine how well they meet the criteria and constraints of the problem.