Instructional cost

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed education

10% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 5 + 5 + 0 + 0 + 0 = 10/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: Reusable tutoring systems offer an efficiency mechanism, but full dollars and teacher minutes per retained mastery success are still missing. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Measure setup, licensing, monitoring and correction costs plus teacher minutes; divide by students passing delayed unaided mastery tests. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Total dollars and teacher minutes per student reaching retained mastery, including onboarding, monitoring, and correction.

Evidence requirements

Use randomized or otherwise well-controlled comparisons against competent instruction. Separate assessment creation from tutor design, prevent answer leakage, and test students without the tool. Measure dropout and intention-to-treat outcomes, not just outcomes among enthusiastic completers. Track incorrect instruction and dependence on assistance. Student satisfaction and fluent homework are not substitutes for demonstrated learning.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Implementation effort documented; cost per retained mastery unknown. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

June 3, 2025; experiment fall 2023: Preparing individual AI-supported lessons took days, while initial tutor-platform development took months. AI tutoring randomized crossover study.

Interpretation and tracking value

These qualitative durations are not total dollars or teacher minutes per retained-mastery success. Platform development must be amortized transparently.

Next comparable measurement

Measure setup, licensing, monitoring and correction costs plus teacher minutes; divide by students passing delayed unaided mastery tests. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.