Lesson 255 of 1550
Piloting AI Tutors: Designing Pilots That Generate Real Decisions
AI tutoring vendors all promise transformative outcomes. Schools that get value design pilots that test specific claims with rigor — not vendor-friendly demos.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2AI tutor pilot
- 3evidence
- 4RCT
Concept cluster
Terms to connect while reading
Section 1
The premise
Most AI tutor pilots produce data nobody can act on; deliberate pilot design creates actionable evidence.
What AI does well here
- Define the specific outcome you'll measure (math achievement, time-on-task, confidence) before pilot start
- Design comparison structure — pilot class vs control class, or pre/post within same students
- Set thresholds for adopt / extend / reject decisions before seeing data
- Run pilots for at least one full unit/quarter — short pilots produce noise, not signal
What AI cannot do
- Substitute for the teacher's classroom expertise about what works in your context
- Replace the family conversation about whether AI tutoring fits your community
- Generate generalizable conclusions from a single pilot
Key terms in this lesson
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