# Higher education workbench guide

Read [Judgment in Higher Education](https://judgmentlab.net/?audience=he#he-essay), the full companion edition for this setting. Its text is included in the matching site-generated Design context. Use the original officer essay for comparisons with PME.


Status: Proposed HE adaptation — awaiting discipline-specific educator trials and classroom evidence.

Design work where students direct AI toward a standard they choose and defend it with disciplinary evidence.

## Readiness and responsibility

Identify the course level and what students can already read in a source: what a study measured, whom it followed, and for how long. Check this in the learner's unaided frame, teach it there when it is missing, then continue. Directing several agents is an advanced target, not an entry requirement.

Students own the standard their work uses and the claims that follow from it. Instructors own the reading list, the assignment, and assessment. The firm's policy decision is outside the exercise.

## Worked teaching example

### Return-to-office research memo

This is an authored design example built on real, cited sources; the AI output is constructed for practice, not a captured model response or a classroom result. Offer it as an optional starting point; the educator may supply a different task.

A mid-sized software firm, most of whose recent hires are new graduates, is deciding whether to require office work. The reading list: Bloom et al. (2015), where Ctrip call-center volunteers working from home performed 13% better and were promoted less at equal performance; Bloom, Han & Liang (2024), where a hybrid trial with 1,612 Trip.com employees cut quits by a third with no effect on performance reviews; and Emanuel, Harrington & Pallais (2023), where junior engineers received less code feedback when teammates were not nearby. The constructed AI synthesis summarizes each accurately and concludes that the evidence is mixed and hybrid is the balance. Its frame treats productivity as short-run output averaged across workers; for this firm, the feedback and promotion findings decide the question.

### Interactive sequence

1. Before AI enters, ask the learner what productivity should mean for this firm and what evidence would decide the question. Then have the learner direct AI against that frame, for example by sorting the studies by what they measured and whom they followed, or by having one agent argue for managers and another for new hires, and record what they kept.
2. Ask one question at a time and wait. Show the constructed misframed answer after the learner's own frame and AI-directing step; let the educator accept, check, revise, or refuse with reasons. Provide assistance when requested and record it.
3. The firm is instead an established call center whose staff average ten years of experience and are rarely promoted out of their roles. Ask which evidence matters most now and whether the recommendation changes. Do not accept a repeated "juniors need proximity" answer.
4. Save the unaided frame, one AI contribution kept and one refused with reasons, the diagnosis of the synthesis's frame, the changed-case response, and the educator's follow-up.

## Reference matrix

These are proposed practices for this setting. Choose by learning objective and task readiness. The six phases are a design lens, not a validated developmental sequence; later phases are not automatically better.

| Practice | Learner | Educator | Institution |
| --- | --- | --- | --- |
| Ask | State what the key term should mean for this case before asking AI anything. | Supply the case and reading list; leave the standard open. | Provide approved access and assignment expectations. |
| Understand | Explain what each source measured, whom it followed, and for how long. | Teach study design inside the unaided frame step when it is missing. | Provide accessible source formats and learning support. |
| Produce | Direct AI with a stated purpose and criteria; record what was kept and why. | Model a structured request and an evaluator loop on a different question. | Clarify allowed assistance and attribution. |
| Judge | Diagnose the misframed synthesis, revise, and defend the result against a changed case. | Grade the defended frame, not the conclusion. | Allow process evidence without excessive marking burden. |
| Codify | Write a reusable check: what question did each source answer? | Test it on another assignment or discipline. | Keep a reviewed library of misframed answers with each frame flaw named. |
| Supervise | Advanced: assign agents distinct roles and moderate their disagreement. | Use once students direct a structured request well. | Set tool and data boundaries; retain instructor assessment authority. |

## Evidence needed before broader use

Two instructors review a handful of records from one assignment against the course objective. Check that the learner's defended standard, rather than generic AI vocabulary, explains the assessment. Record marking time and reviewer disagreement. A later task with a different firm or discipline is needed to examine transfer.

Record the setting, subject, task, participants’ roles, support, observed evidence, reviewer disagreement, and limits. Evidence from one audience does not validate another. A software check or an educator rehearsal does not establish student learning.

Keep the essay, claims, and source spine as the original argument and evidence. These examples are teaching proposals derived from the shared method; they add no claim of demonstrated effectiveness. Jev remains a separate evaluation workstream.