# Judgment Lab: the shared foundation

Judgment Lab helps educators teach and inspect human judgment in AI-enabled work. A strong finished product matters. It does not, by itself, establish what the learner understood, chose, checked, or could do when the situation changed.

The Irreducible Officer develops that argument for professional military education. These audience guides adapt its method for higher education and high school. They are teaching designs to test, not evidence that the essay has already been validated across education.

## The common practice

Start with a purpose and a consequential choice. Examine the information and framing you inherited, including AI-shaped inputs. Direct useful assistance, decide what to accept or change, and explain the reasons. Change a condition to see what the reasoning depends on. Save a small record of decisions that another educator can inspect and improve.

AI can propose a purpose, frame, criterion, or alternative. People authorize the choices that govern the work and remain responsible for them. Ownership does not require originating every idea unaided. Nor does it follow from signing a disclosure statement.

## Foundations make judgment possible

A learner needs enough subject knowledge to understand a claim, enough reasoning to connect evidence to that claim, and enough awareness of the task to notice when help changes it. These capacities develop through instruction and practice. They should not be assumed from age, seniority, credentials, or fluent prose.

Build those capacities inside the work rather than as a gate before AI enters. For each task, identify one or two prerequisites, observe them in the learner's unaided first attempt, and teach them there when needed. A teacher-supplied frame can give a novice room to make a meaningful choice. More experienced learners may be ready to contest the frame itself. Expand responsibility from evidence of readiness, not from a fixed age ladder.

Some effort is the point of the lesson. Search, calculation, drafting, or synthesis may be developmental work in one task and avoidable overhead in another. Decide which capability the assignment is building before deciding which effort AI should remove. Preserve access supports and distinguish help communicating a decision from help making it.

## What changes by audience

| Setting | Consequential choice | Foundations to check | Educator responsibility |
| --- | --- | --- | --- |
| PME | Define the professional problem, weigh risk and competing interests, direct staff or AI assistance. | Domain knowledge, strategic logic, assumptions, source interpretation. | Observe defensible reliance and judgment under changed conditions; retain professional context and authorization. |
| Higher education | Choose and defend the standard behind a disciplinary inference, interpretation, design, or recommendation; direct AI toward it. | Relevant concepts, methods, source standards, and what a claim requires in that discipline. | Align assistance and assessment to the learning objective; choose feasible and accessible evidence. |
| High school | Choose and defend a standard or question within a teacher-supplied task; direct AI through structured prompts or teacher-run evaluator loops. | Task vocabulary, subject knowledge, representations, and the reasoning required by this particular decision. | Teach missing foundations, choose materials and AI access, and retain adult responsibilities. |

High school is the first K–12 starting point. Middle and elementary adaptations remain future work: revisit the concepts, task size, scaffolding, language, teacher mediation, and evidence rather than shrinking the same text.

## Practice before designing

The educator starts by interacting with the essay in the failure-mode lab. The assistant asks one question at a time, waits for decisions, offers a contestable contribution, and changes a condition. Then the educator adapts the method to an actual teaching objective. Students do not need to read the officer essay or have their own AI accounts.

The workbench supports that adaptation: assignment design, assessment, flawed contributions, source kits, calibration, after-action notes, method cards, and bounded delegation. Its six-phase progression is a design lens, not a validated developmental scale or a requirement that every learner reach agent supervision.

## Evidence and reuse

Keep the initial decision, one accepted or changed contribution and its reason, the response to a changed condition, and the educator's next teaching decision. Mark any hints or demonstrations. A conversation shows what happened with those supports. It does not establish retention, causal improvement, or general competence.

A colleague can inspect the same evidence and disagree. Preserve the disagreement and revise the exercise or criteria when needed. Save reviewed examples and the reason for each change; do not make the trace a paperwork exercise.

## Relationship to the spine

The eight named claims remain in claims.md: changed performance, limits of finished artifacts, purpose through frame, appropriate reliance, developmental friction, structural accountability, observable ownership, and educator practice that compounds. Audience adaptations qualify how these are taught and observed. They do not replace the original claim map or turn source notes into findings from new settings.

Jev is a separate proposed evaluation workstream. The current lab requires no Jev service and supplies no automated grades or judgment score. Its possible future contribution should be evaluated against educator-owned criteria and recorded disagreements before it affects instructional decisions.

## Supporting reading

See `sources/audience-foundations.md` for the research supporting explicit foundations, modeling, and subject-specific practice, with limits on what it establishes. The original `sources/source-spine.md` remains the essay’s evidence map.