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Longhand AI competency course

TDARTurn AI effort into a result you can measure.

Start with the business result, preserve the human decision, and decide how you will know whether the AI contribution actually helped.

Version 1.070 minutesPractice evidence required

The finish line

A reviewable TDAR Results Brief

Your brief names the result, baseline, AI contribution, human contribution, evidence, and remeasurement date. It is practice evidence—not a certified result.

Progress is saved in this browser.

Longhand AI competency course

TDARTurn AI effort into a result you can measure.

Start with the business result, preserve the human decision, and decide how you will know whether the AI contribution actually helped.

Version 1.070 minutesPractice evidence required

The finish line

A reviewable TDAR Results Brief

Your brief names the result, baseline, AI contribution, human contribution, evidence, and remeasurement date. It is practice evidence—not a certified result.

Progress is saved in this browser.

Measurable learning objectives

By the end, you can produce evidence—not just repeat the framework.

Distinguish a measurable result from AI activity, output volume, or tool adoption.

Construct a TDAR brief with a result, baseline, AI contribution, human contribution, and remeasurement date.

Identify missing evidence and rewrite a plausible but non-measurable AI proposal.

Define the artifact and measurement record needed for a later Opportunity Diagnosis assessment.

You will see a worked example, diagnose a weak example, complete guided practice, and leave with an independent practice artifact. The artifact prepares you for a later human-scored performance assessment.

Course requirements

Know what you need—and what counts as evidence.

Prerequisites

One recurring workflow you understand or the incident-report practice case in this course.
Access to a trustworthy baseline source—or permission to label the baseline as unknown and plan how to measure it.
Authority to propose an experiment; implementation authorization is not required for this lesson.

Assessment connection

Milestone 1 — Opportunity Diagnosis · planned

A completed TDAR Results Brief
A traceable baseline or a justified baseline-collection plan
A dated remeasurement plan with accountable human review

Lesson practice is formative. This course does not issue proficiency or certification; the listed performance assessment requires separate evidence and qualified human evaluation.

Authoritative sources and course version

Version 1.0 · estimated 70 minutes · sources reviewed 2026-08-10

Higher Education Rubric, Seventh EditionQuality Matters · Constructive alignment among objectives, assessment, materials, activities, and technology.OSCQR SUNY Online Course Quality Review RubricSUNY Online · Online-course review, authentic assessment, interaction, feedback, support, and accessibility.Artificial Intelligence Risk Management Framework (AI RMF 1.0)National Institute of Standards and Technology · Govern, Map, Measure, and Manage functions and trustworthy AI characteristics.

The guide

Explain it. See it. Challenge it. Apply it. Save the evidence.

Step 1 of 5 · 10 min

Define the complete Results Formula before choosing a tool.

Start with the five fields

An AI initiative can produce impressive activity and no defensible return. TDAR makes the result, comparison point, division of work, and measurement decision explicit before implementation creates sunk cost.

Do this

1Result: name the business or service outcome as a number, rate, time, quality measure, or observable state—not as 'use AI.'
2Baseline: identify the current value, source, period, and conditions so the later comparison is meaningful.
3AI contribution: state the bounded part of the workflow the system will perform or assist.
4Human contribution: state the judgment, approval, relationship, accountability, or exception handling that remains human.
5Remeasurement: set the date, sample, owner, and comparison rule before the experiment begins.

Remember: TDAR is a Longhand-developed results record. The rigor comes from the quality of the evidence and decision rules, not the acronym itself.

What you leave with

Five-field TDAR draft

Missing-evidence list

TDAR field test

Field	Question	Reject when
Result	What changes for the work or customer?	It only measures tool use or output volume.
Baseline	Compared with what, from which source and period?	The number is guessed or conditions differ materially.
AI contribution	What bounded work does the system perform?	The claim is 'AI improves everything.'
Human contribution	Which judgment and accountability stay human?	Review is vague or only happens after harm.
Remeasurement	When, on what sample, using what rule?	Success will be declared after seeing the result.

Step 1 of 5 · Run it

Apply it before you move on

Do this

1Result: name the business or service outcome as a number, rate, time, quality measure, or observable state—not as 'use AI.'
2Baseline: identify the current value, source, period, and conditions so the later comparison is meaningful.
3AI contribution: state the bounded part of the workflow the system will perform or assist.
4Human contribution: state the judgment, approval, relationship, accountability, or exception handling that remains human.
5Remeasurement: set the date, sample, owner, and comparison rule before the experiment begins.

Remember: TDAR is a Longhand-developed results record. The rigor comes from the quality of the evidence and decision rules, not the acronym itself.

Draft the five fields

Help me draft a TDAR Results Brief for this recurring workflow. Ask for one missing fact at a time. Do not invent a baseline. Use these fields: measurable result, current baseline and source, bounded AI contribution, accountable human contribution, and remeasurement date/sample. End with a section called 'Evidence still missing.'

Workflow: [describe it]
Current evidence: [paste or say unknown]

What you leave with

Five-field TDAR draft

Missing-evidence list

Practice evidence target

You can explain all five fields in plain language and point to a result that could change even if the AI tool stayed the same.

Step 1 checkpoint

Start with the five fields: practice checkpoint

Practice evidence target

You can explain all five fields in plain language and point to a result that could change even if the AI tool stayed the same.

Recording this checkpoint saves self-reported lesson practice in this browser. It is not evidence of certified competency; certification requires a separately submitted artifact and qualified human evaluation.

Step 2 of 5 · 12 min

Use comparable observations, not a convenient guess.

Build a baseline you can defend

Without a baseline, a team can celebrate faster work while error correction, review time, or downstream cost quietly increases. A baseline defines the comparison and exposes what is still unknown.

Do this

1Choose a result measure already connected to the work: cycle time, first-pass accuracy, corrections, throughput, cost, satisfaction, or another operational measure.
2Name the source, period, sample size, exclusions, and unit. Preserve the raw record or a reproducible query when permitted.
3Record companion measures that could reveal displacement, such as faster drafting paired with more review corrections.
4If evidence is unavailable, say 'baseline unknown' and specify a collection window. Do not turn an assumption into a historical fact.

Remember: Use median rather than mean when a few extreme cases would distort normal workflow time, and preserve the original observations so the choice can be reviewed.

What you leave with

Baseline record

Quality guardrail

Collection plan for unknowns

Minimum baseline record

Element	Example
Measure	Median minutes from source close to approved incident report
Source	Timestamped incident and document history
Period/sample	Eight weekly reports before the experiment
Guardrail	Number of material corrections after first review
Exclusions	Major incidents requiring a separate review process

Step 2 of 5 · Run it

Apply it before you move on

Do this

1Choose a result measure already connected to the work: cycle time, first-pass accuracy, corrections, throughput, cost, satisfaction, or another operational measure.
2Name the source, period, sample size, exclusions, and unit. Preserve the raw record or a reproducible query when permitted.
3Record companion measures that could reveal displacement, such as faster drafting paired with more review corrections.
4If evidence is unavailable, say 'baseline unknown' and specify a collection window. Do not turn an assumption into a historical fact.

Remember: Use median rather than mean when a few extreme cases would distort normal workflow time, and preserve the original observations so the choice can be reviewed.

Audit the baseline

Audit this proposed AI baseline. Separate observed facts from estimates, identify comparability problems, and list the minimum data needed before a result claim is credible. Do not fill missing values.

Proposed baseline: [paste]
Planned result: [paste]

What you leave with

Baseline record

Quality guardrail

Collection plan for unknowns

Practice evidence target

Your baseline includes a value or explicit unknown, source, period, sample, unit, exclusions, and at least one quality guardrail.

Step 2 checkpoint

Build a baseline you can defend: practice checkpoint

Practice evidence target

Your baseline includes a value or explicit unknown, source, period, sample, unit, exclusions, and at least one quality guardrail.

Step 3 of 5 · 12 min

Place review where judgment occurs and avoid crediting the system for the whole result.

Separate AI contribution from human accountability

Results usually come from a system of people, data, process, and technology. A credible TDAR brief makes the AI contribution narrow enough to test and keeps accountable judgment with the person authorized to make it.

Do this

1Walk the workflow step by step and identify which inputs the AI may access and which actions it may propose or perform.
2Mark human judgment at the step where significance, truth, risk, relationship, or professional authority is decided—not only at the end.
3State what deterministic checks or source verification must pass before the human review.
4List external changes the system may not make without explicit approval, including sending, filing, publishing, purchasing, deleting, or changing a system of record.

Remember: Human review is a designed control only when the reviewer has the information, time, authority, and criteria needed to intervene.

What you leave with

Contribution map

Approval rules

Stop conditions

Contribution boundary

Contributor	Good fit	Must not be implied
AI	Draft, extract, classify, compare, flag	Final truth, professional authority, or accountability
Human	Interpret, decide, approve, communicate, own consequences	Passive rubber stamp after all decisions are made
Deterministic system	Validate schema, totals, permissions, invariants	Judgment about meaning or acceptable risk

Step 3 of 5 · Run it

Apply it before you move on

Do this

1Walk the workflow step by step and identify which inputs the AI may access and which actions it may propose or perform.
2Mark human judgment at the step where significance, truth, risk, relationship, or professional authority is decided—not only at the end.
3State what deterministic checks or source verification must pass before the human review.
4List external changes the system may not make without explicit approval, including sending, filing, publishing, purchasing, deleting, or changing a system of record.

Remember: Human review is a designed control only when the reviewer has the information, time, authority, and criteria needed to intervene.

Map the contributions

Map this workflow into three columns: bounded AI contribution, accountable human contribution, and deterministic validation. For each external or high-impact action, add the required approval and a stop condition. Do not assume the AI has authority.

Workflow: [paste steps]

What you leave with

Contribution map

Approval rules

Stop conditions

Practice evidence target

Every material workflow step has an accountable AI, human, or deterministic-system contribution and an explicit approval rule for external effects.

Step 3 checkpoint

Separate AI contribution from human accountability: practice checkpoint

Practice evidence target

Every material workflow step has an accountable AI, human, or deterministic-system contribution and an explicit approval rule for external effects.

Step 4 of 5 · 14 min

Inspect a complete example and diagnose the tempting weak version.

Contrast a result with an activity claim

Weak proposals often sound strategic because they mention speed or productivity. The difference is whether the result, evidence, contributions, and decision rule are specific enough for another reviewer to challenge.

Do this

1Read the weak proposal and identify every field that cannot be independently checked.
2Compare it with the complete incident-report example and locate the baseline, quality guardrail, contribution boundary, and remeasurement rule.
3Test the conclusion: decide what result would cause the team to continue, revise, or stop the experiment.
4Rewrite one weak field from your own proposal before opening the independent task.

Remember: The example does not prove the proposed result. It creates a fair test that could disconfirm the idea.

What you leave with

Weak-claim diagnosis

Corrected TDAR fields

Worked incident-report TDAR

Field	Reviewable version
Result	Reduce median preparation time for the weekly approved report from 150 to 75 minutes while keeping material post-review corrections at or below the baseline.
Baseline	Eight weekly reports; median 150 minutes; three contained a material post-review correction. Source: timestamped report history.
AI contribution	Draft chronology and standard sections from authorized incident records; cite each source; flag missing fields.
Human contribution	Incident lead decides significance, checks cited records, edits causal language, and approves release.
Remeasurement	After four weekly reports, compare median time and correction rate. Continue only if time improves and the correction guardrail does not worsen.

Step 4 of 5 · Run it

Apply it before you move on

Do this

1Read the weak proposal and identify every field that cannot be independently checked.
2Compare it with the complete incident-report example and locate the baseline, quality guardrail, contribution boundary, and remeasurement rule.
3Test the conclusion: decide what result would cause the team to continue, revise, or stop the experiment.
4Rewrite one weak field from your own proposal before opening the independent task.

Remember: The example does not prove the proposed result. It creates a fair test that could disconfirm the idea.

Diagnose the weak claim

Weak proposal: 'Use AI to make incident reporting 50% more productive.'

List what cannot be verified, what evidence is missing, what could be displaced, and how you would rewrite each TDAR field. Do not accept the 50% claim without a baseline and defined measure.

What you leave with

Weak-claim diagnosis

Corrected TDAR fields

Practice evidence target

You can explain why the strong example supports a future decision while the weak example only records activity.

Step 4 checkpoint

Contrast a result with an activity claim: practice checkpoint

Practice evidence target

You can explain why the strong example supports a future decision while the weak example only records activity.

Step 5 of 5 · 22 min

Apply the model to a new workflow and preserve the decision evidence.

Produce your independent TDAR brief

Competence must transfer beyond the worked example. The independent brief asks you to make the evidence and uncertainty visible without step-by-step answers.

Do this

1Choose a recurring workflow with an authorized practice context. If you cannot use workplace information, use the provided incident-report case with different targets and justify the changes.
2Complete all five TDAR fields and attach or describe the baseline source. Label every estimate and unknown.
3Add one quality, safety, or customer guardrail that prevents apparent improvement from hiding a worse outcome.
4Write continue, revise, and stop decision rules for the remeasurement date.
5Self-check the brief for outcome value, evidence quality, reliability, safety, and reflection. Save the artifact for later assessment preparation.

Remember: Do not paste confidential or regulated information into an unapproved system. A synthetic or de-identified case can demonstrate the same reasoning for practice.

What you leave with

Independent TDAR Results Brief

Self-check

Saved evidence location

Step 5 of 5 · Run it

Apply it before you move on

Do this

1Choose a recurring workflow with an authorized practice context. If you cannot use workplace information, use the provided incident-report case with different targets and justify the changes.
2Complete all five TDAR fields and attach or describe the baseline source. Label every estimate and unknown.
3Add one quality, safety, or customer guardrail that prevents apparent improvement from hiding a worse outcome.
4Write continue, revise, and stop decision rules for the remeasurement date.
5Self-check the brief for outcome value, evidence quality, reliability, safety, and reflection. Save the artifact for later assessment preparation.

Remember: Do not paste confidential or regulated information into an unapproved system. A synthetic or de-identified case can demonstrate the same reasoning for practice.

Independent TDAR brief template

# TDAR Results Brief

## Workflow and decision
[What recurring work is being considered, and what decision will this experiment inform?]

## Measurable result
[Measure, target, unit, population, and time period]

## Baseline
[Current value or 'unknown'; source; period; sample; exclusions; guardrail]

## Bounded AI contribution
[Authorized inputs, work performed, outputs, and prohibited actions]

## Accountable human contribution
[Judgments, reviews, approvals, and owner of consequences]

## Remeasurement
[Date, sample, comparison method, continue/revise/stop rules]

## Evidence still missing
[Facts that must be collected or verified]

## Reflection
[Which assumption is most likely to change the decision?]

What you leave with

Independent TDAR Results Brief

Self-check

Saved evidence location

Practice evidence target

You saved a TDAR brief another reviewer can inspect without asking what the result, baseline, contribution boundaries, evidence, or remeasurement rule means.

Step 5 checkpoint

Produce your independent TDAR brief: practice checkpoint

Practice evidence target

You saved a TDAR brief another reviewer can inspect without asking what the result, baseline, contribution boundaries, evidence, or remeasurement rule means.

Formative practice complete

You now have a testable result—not an AI activity claim.

Keep the TDAR brief with its source and unknowns. Phase 3 will place it inside the human-scored Opportunity Diagnosis assessment; completing this lesson alone does not establish proficiency.

Next course

The Decision-Making Process

Use the result and evidence to make a defensible build, defer, or reject decision.

Open training

Review your gaps

Return to the AI Gear Audit

Update your formative self-rating and open the next direct lesson. Green remains self-reported, not certified.

Open training