What You Will Learn

• How AI can transform evaluation evidence into findings and reports.
• Why a polished report is not necessarily a methodologically defensible report.
• What evaluators mean by accuracy, evidence support and completeness.
• Why checking an AI report may reveal errors but still miss important omissions.
• How precision and recall provide a useful framework for AI-assisted evaluation QA.
• Why professional evaluation judgement remains important.

1. The Rise of the AI-Generated Evaluation Report

AI systems can now process large quantities of qualitative and documentary evidence and produce highly polished outputs. An evaluator or commissioning officer can provide an evaluation question, Terms of Reference, interviews, reports and other source material and ask an AI system to analyse the evidence and draft findings.

The resulting report may contain:

• clear findings;
• quotations from participants;
• references to source documents;
• themes and patterns;
• comparisons between groups;
• conclusions; and
• recommendations.

2. What Does “Accurate” Actually Mean?

When evaluators describe an AI-generated report as accurate, they may be referring to several different things.

These dimensions should not be treated as interchangeable. A report can be factually accurate while still being incomplete or methodologically weak.

3. The AI Evaluation Workflow

A simple AI-assisted workflow might look like this:

This workflow can be useful. But it creates a methodological question:

If the answer is simply “the AI read it and produced the report,” the evaluator may have limited visibility into the analytical process.

4. The AI Checking Illusion

A natural response is to ask another AI to check the first AI’s report.

This can be useful. But notice the direction of the check.

That is valuable for checking reported claims. But it does not necessarily ask:

5. Worked Example: 24 Farmers

Imagine an evaluation involving 24 farmer interviews.

The AI-generated report states:

A second AI checks the statement and finds:

• F3 did credit the training.
• F7 did credit the training.
• The quotations are accurate.
• The farmers were correctly identified.

The report has not necessarily said anything false. But it has omitted relevant evidence.

6. Precision and Recall

The example illustrates a fundamental distinction used in information retrieval and classification.

7. Why the Report That Says More Can Do Worse

Compare these two findings.

Finding B contains much more information. But it also creates a stronger obligation to examine the entire population of 24 farmers.

If F9 also credited the training, Finding B is demonstrably wrong.

Finding A can remain technically true even when several additional farmers made the same observation.

8. Why AI May Drift Toward Easy-to-Verify Findings

When AI is asked to produce a report without a sufficiently explicit analytical structure, it has to make many decisions:

• What evidence is relevant?
• Which themes matter?
• Which participant accounts should be highlighted?
• Which findings deserve attention?
• How much contradictory evidence should be included?
• How should ambiguous evidence be handled?

These decisions are part of evaluation methodology, not simply writing.

A report may therefore gravitate toward findings that are:

• easy to verify;
• easy to quote;
• clearly expressed;
• difficult to falsify.

But the easiest findings to verify are not necessarily the findings that best answer the commissioner’s question.

9. A Better Way to Think About AI in Evaluation

The goal should not be to prevent AI from participating in evaluation.

Instead, think of AI as part of a transparent evidence workflow.

The evaluator’s role is therefore not simply to write the final paragraph. It includes designing the evidence pathway that makes the paragraph defensible.

10. What AI Can Do Well—and What Requires Professional Judgement

11. The Evaluator Still Matters

As AI becomes more capable, some of the evaluator’s most important responsibilities move further upstream.

• Operationalise the evaluation questions. Turn broad questions into answerable analytical tasks.
• Choose the method. Match the analytical approach to the evaluation purpose and context.
• Challenge the evidence. Look deliberately for contradictory, missing and uncomfortable evidence.
• Understand context. Interpret evidence within programme and stakeholder realities.
• Assess sufficiency. Determine whether the evidence is strong enough for the conclusion.
• Take responsibility. Be able to explain and defend the methodology and conclusions.

12. A Practical AI-Assisted Evaluation Workflow

1. Define the evaluation questions. Clarify exactly what the evaluation needs to answer.
2. Define evidence requirements. Specify what kinds of evidence can answer each question.
3. Structure the evidence. Identify sources, participants, documents and relevant metadata.
4. Use AI for evidence analysis. Extract, classify, compare and organise evidence with an explicit process.
5. Generate evidence-linked findings. Build findings from identifiable evidence.
6. Perform precision QA. Check whether the claims in the report are supported.
7. Perform recall QA. Check whether important evidence was omitted.

13. Interactive Exercise

14. Try This With AI

Use the following prompt as an introductory precision-checking exercise.

15. Evaluator Checklist

16. The Key Lesson

Continue the Series

Research and Further Reading