Public lesson preview

AI for MEAL, reporting, and learning.

This preview follows the complete seven-part professional journey from a controlled evidence workroom to an auditable MEAL Learning Package.

Request professional enrollment Download workflow map

A humanitarian MEAL team reviewing field evidence and programme decisions — Evidence becomes useful when sources, methods, limitations, review, and decisions remain visible together.

Orientation

Build the evidence workroom

Define the decision, evidence packet, role map, safe-use boundary, audit trail, and stop conditions before AI enters the workflow.

Named data and decision owners.
Approved sources and environment.
Reconstructable review.

Lesson 1

Prepare safe evidence for synthesis

Classify data, minimize detail, assess disclosure risk, authorize the use, and control the full lifecycle of inputs and outputs.

Purpose and authority.
De-identification limits.
Evidence-packet checklist.

Lesson 2

Move from evidence to bounded findings

Build provenance, qualitative codebooks, reviewed samples, exception logs, and clear distinctions between evidence and interpretation.

Source-linked themes.
Negative and minority cases.
Triangulation without false certainty.

Lesson 3

Reconcile indicators and donor reporting

Review definitions, targets, actuals, variance, missingness, context, contribution claims, and management response against controlled sources.

Valid comparison checks.
Claim review matrix.
Truthful variance explanation.

Lesson 4

Turn findings into learning and action

Create learning briefs and after-action records that connect bounded evidence to authorized, owned, dated, and reviewable actions.

Decision question and audience.
Action ownership.
Community feedback closure.

Lesson 5

Review bias, context, and missing voices

Test coverage, selection, measurement, language, coding, power, minority views, and consequences before findings influence decisions.

Representation review.
Translation and access bias.
Corrective action and residual limits.

Lesson 6

Defend the MEAL Learning Package

Assemble the data boundary, provenance, method, findings, reporting, bias review, actions, AI-use record, and final approvals.

Nine required components.
Three tabletop tests.
Professional evidence boundary.

Evidence boundary

AI can help organize learning, but people remain responsible for evidence.

This preview supports professional development. It does not replace evaluation design, donor approval, data-protection review, safeguarding judgment, community accountability, or human verification of findings.

Resources

Use the preview with eleven working resources.

Workflow mapMap reporting, synthesis, learning, and review workflows before using AI. Synthesis safety checklistCheck bias, traceability, confidentiality, and limits before using summaries. Facilitator session planWorkshop structure for MEAL teams and organization cohorts. Evidence provenance registerTrace source, method, quality, authorization, approved use, and retention. Codebook and exception logKeep coding rules, translation issues, overlap, negative cases, and source references visible. Indicator variance analysisReview valid comparison, quality, context, explanation, action, and next evidence. Report claim reviewTrace every material claim to source, method, scope, limitations, and decision. Learning action logRecord authority, ownership, resources, evidence of completion, and follow-up.