The allocation problem
7,500 credits is plenty —
if you use it in the right places.
Schools that consistently exhaust their monthly credits are almost always using AI for every tool at maximum frequency. The strategic approach is different: apply AI where it removes the most friction and produces the most improvement per credit. Not every lesson needs a generated plan. Not every class needs a generated exit quiz.
The high-value targets
Apply AI here first.
Then expand if credits allow.
Priority 1 — Exit quiz generation for every assessed lesson (50 credits each)
Exit quizzes are the highest-value-per-credit use of AI. A well-designed 3-question formative check takes 20–30 minutes to write from scratch. AI generates one in 30 seconds for 50 credits. For a school running 3 lessons per teacher per week, this costs 450 credits/teacher/week.
Priority 2 — Lesson regeneration for lessons that produced poor exit data (200 credits each)
When an exit quiz reveals that more than 40% of students could not answer Q3, the lesson needs improving before tomorrow. AI regeneration of the specific element that failed takes 2 minutes and 200 credits.
Priority 3 — Course outline generation for new units (300 credits each)
A 6-week course outline costs 300 credits and saves 3–4 hours of planning time. Use AI for every new unit rather than for every lesson within existing units.
A weekly credit budget
How to allocate 7,500 credits
across a typical teaching week.
Weekly allocation — 2 teachers, 8 active students
Student credits (8 × 750 ÷ 4 weeks): ~1,875 credits/week
Exit quizzes (3 per teacher × 2 teachers): 300 credits/week
Lesson regenerations (1–2 per week across both teachers): 200–400 credits/week
Total: ~2,375–2,575 credits/week — leaves ~850 credits buffer
Exit quizzes (3 per teacher × 2 teachers): 300 credits/week
Lesson regenerations (1–2 per week across both teachers): 200–400 credits/week
Total: ~2,375–2,575 credits/week — leaves ~850 credits buffer
You've finished C3