Research library
Education and skill development
View the four-date completion graphs and reconstruction notes.
Promise and evidence
AI could make effective individualized instruction more accessible and shorten the time needed to acquire useful skills. A randomized study of an AI tutor in undergraduate physics provides a concrete evaluation example. Results from a particular course and tutor design should not be generalized to all students, subjects, or educational goals.
Apply the shared measurement protocol. Targets are proposed.
Dated baseline coverage
All linked measurable-outcome files below contain an evidence baseline reviewed November 7, 2026, with source dates, findings, limitations and follow-up measurements. These distinguish measured results from proxies and unknown values; they do not assert that proposed milestones are achieved. See the baseline coverage notes.
Progress estimates
Judgment-based estimates as of November 7, 2026, using the same 0–100% scale and five-part rubric as mathematics. 0% anchors the period before ChatGPT's public release in 2022; 100% requires AI to manage this field's continuing advancement without human participation. Confidence is low. These are evidence-based judgments, not empirical completion measurements, forecasts or percentages of jobs replaced.
| Measurable outcome | Estimated progress | Execution / breadth / agenda / validation / improvement (each out of 20) |
|---|---|---|
| Access and disparities | 15% | 5 / 5 / 0 / 5 / 0 |
| Instructional cost | 10% | 5 / 5 / 0 / 0 / 0 |
| Learning gain | 15% | 5 / 5 / 0 / 5 / 0 |
| Retention | 10% | 5 / 5 / 0 / 0 / 0 |
| Time to mastery | 25% | 10 / 5 / 5 / 5 / 0 |
| Transfer | 15% | 5 / 5 / 0 / 5 / 0 |
Each linked outcome explains its score, cites its dated evidence baseline and identifies the next measurement. Zero indicates no qualifying post-anchor progress established by the reviewed evidence for that outcome, not proof of no relevant capability. Do not average these overlapping indicators into a field-wide percentage or interpret their differences as a precise ranking of fields. All outcomes reaching 100% would still require a separate field-wide demonstration of sustained autonomous advancement.
Measurable features
| Feature | Measurement and acceptance rule |
|---|---|
| Learning gain | Difference in independently administered assessment scores after adjusting for baseline, with absolute and standardized effects and uncertainty. |
| Retention | Performance on new equivalent assessments after 30 and 90 days, completed without AI assistance. |
| Transfer | Success on unfamiliar problems, explanations, and practical tasks requiring the learned skill rather than memorized answers. |
| Time to mastery | Student hours needed to reach a frozen mastery criterion, including failed attempts; keep non-completers in the analysis. |
| Instructional cost | Total dollars and teacher minutes per student reaching retained mastery, including onboarding, monitoring, and correction. |
| Access and disparities | Participation, completion, and learning outcomes by relevant student groups, prior attainment, language, and accessibility needs. |
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.
Proposed milestones
In a prospectively powered study, improve unaided learning by at least 0.2 standard deviations or reduce time to the same mastery level by at least 20%; preregister which is primary.
Preserve a meaningful advantage on 90-day retention and novel transfer tasks, with no subgroup falling outside a prespecified acceptable margin.
Replicate across three institutions and at least two subjects with independent evaluators.
Demonstrate at least 20% lower cost per student attaining retained mastery at scale. To connect education to accelerating technological development, separately measure whether trained learners subsequently perform verified research or engineering work more effectively.