EvalCommunity Tutorial

How to Use AI in Evaluation Practice: Chatbots, Copilots, Embedded Tools, and Automated Workflows

A practical guide for evaluators on using AI responsibly across evaluation design, data collection, analysis, reporting, learning, and automated workflows.

Tutorial Summary

This tutorial explains how monitoring, evaluation, accountability, and learning professionals can use AI in evaluation practice through three main modes: direct AI applications, AI embedded in existing software, and automated AI-supported workflows.

The focus is practical and responsible use: human review, data protection, evidence traceability, bias checks, and professional evaluation judgment.

What You Will Learn

• How AI is used in evaluation practice.
• The difference between chatbots, copilots, embedded AI tools, AI workflows, and AI agents.
• Where AI can support evaluation design, data collection, qualitative analysis, reporting, and learning.
• How to use AI for drafting, summarization, translation, coding support, evidence review, and stakeholder feedback analysis.
• How to reduce risks such as hallucination, bias, confidentiality breaches, weak traceability, and automation bias.
• How to document AI use transparently in evaluation reports.

Authoritative Sources Used

This tutorial is based on official product and documentation pages from AI and evaluation-related software providers. Because AI tools change quickly, users should verify current features, pricing, privacy terms, and data protection conditions directly with each provider before using AI with sensitive evaluation data.

• OpenAI ChatGPT capabilities overview
• OpenAI File Uploads FAQ
• Microsoft 365 Copilot
• Microsoft 365 Copilot help and learning
• MAXQDA AI Assist
• MAXQDA AI New Code Suggestions
• ATLAS.ti official website
• ATLAS.ti Intentional AI Coding

Responsible AI Use in Evaluation Workflow

Use this workflow whenever AI is introduced into an evaluation task.

Key Principle for Evaluators

AI can assist with evaluation tasks, but it cannot take responsibility for evaluation quality. Evaluators remain accountable for verifying outputs, protecting confidentiality, identifying bias, interpreting evidence, and ensuring findings are appropriate for the evaluation context.

Questions Evaluators Should Ask

• Where in the evaluation process is AI being used?
• What data does the AI system access?
• Does the AI output include traceable evidence?
• Who reviews AI-generated content?
• Could AI-generated summaries change what evidence is seen or ignored?
• Could AI introduce bias or overconfidence into interpretation?

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Step 1: Define the Evaluation Task

Be clear about what AI should support, such as drafting an interview guide, summarizing a report, coding open-ended responses, translating feedback, classifying comments, or preparing a first report outline.

Step 2: Decide Whether AI Is Appropriate

• Is the data sensitive?
• Is consent clear?
• Could AI processing create privacy risks?
• Can the output be verified?
• Is human review available?

Step 3: Prepare the Input

• Remove confidential or unnecessary personal data.
• Provide clear instructions and context.
• Specify the expected output format.
• Ask the AI to separate evidence from interpretation.
• Ask the AI to flag uncertainty and limitations.

Step 4: Generate, Review, and Validate

Use AI to draft, summarize, classify, translate, extract themes, or prepare outlines. Start with a small sample before applying AI to a full dataset.

Practice rule: Use AI outputs as drafts or suggestions, not as final evaluation products.

Basic Prompt Structure

1. Role: Define the role the AI should play.
2. Task: State the task clearly.
3. Context: Provide program, sector, country, or evaluation background.
4. Data: Provide source material or describe what is being analyzed.
5. Output format: Ask for a table, list, memo, draft, or checklist.
6. Quality criteria: Ask for evidence, uncertainty, limitations, and cautions.
7. Boundaries: Ask the AI not to invent facts, sources, or findings.

Example Prompt

You are supporting a monitoring and evaluation team. Review the following open-ended survey responses from a youth employment program. Identify recurring themes related to barriers to employment, but do not create final findings. Organize the output in a table with theme, short description, example quote, and possible M&E implication. Flag uncertainty or weak evidence. Do not include personal identifiers.

• Has the evaluation team documented where AI was used?
• Was sensitive data removed or protected?
• Was consent reviewed?
• Was the output checked against source evidence?
• Were marginalized voices considered?
• Were contradictions and negative cases included?
• Was bias assessed?
• Were AI-generated summaries reviewed by a human evaluator?
• Were final findings developed by human evaluators?
• Was AI use disclosed in the final report where appropriate?

Exercise: Using AI to Support a Small Evaluation Task

Use a small dataset with one project description, five stakeholder comments, one evaluation question, and one monitoring update.

1. Use AI to summarize the project description.
2. Use AI to suggest three evaluation questions.
3. Use AI to classify stakeholder comments into themes.
4. Review and correct the AI-generated themes.
5. Identify what the AI missed.
6. Write one evidence-based finding.
7. Write one AI use statement.

AI tools were used to support selected evaluation tasks, such as drafting, summarization, organization, translation, coding support, or document review. All AI-generated outputs were reviewed, edited, and validated by the evaluation team. Final evaluation findings, conclusions, and recommendations were developed by human evaluators and checked against source evidence. AI was not used as a substitute for professional evaluation judgment.

Can AI replace evaluators?

No. AI can support evaluation tasks, but evaluators remain responsible for judgment, ethics, interpretation, and final findings.

What are the main uses of AI in evaluation?

AI can support drafting, summarization, translation, qualitative coding, evidence review, stakeholder feedback analysis, reporting, and routine workflow automation.

What is the difference between chatbots and embedded AI tools?

Chatbots are used directly through an AI interface. Embedded AI tools are AI features built into software evaluators already use, such as office tools, qualitative analysis software, meeting platforms, or dashboards.

What are AI workflows in evaluation?

AI workflows are structured processes where AI performs repeated tasks such as classifying feedback, summarizing meetings, routing information, or drafting routine updates.

Should AI use be disclosed in evaluation reports?

Yes, especially when AI contributed to analysis, coding, summarization, or drafting. The report should explain how AI was used and how outputs were reviewed.

• The AI task was clearly defined.
• Sensitive data was removed or protected.
• Consent and data protection requirements were reviewed.
• The AI output was checked against source evidence.
• Human reviewers corrected errors.
• Bias and omissions were considered.
• Minority and marginalized voices were not excluded.
• Findings were traceable to evidence.
• AI-generated content was not used as final analysis without review.
• AI use was documented transparently.

Conclusion

AI is becoming part of evaluation practice through chatbots, copilots, embedded tools, and automated workflows. These tools can help evaluators work more efficiently by supporting drafting, summarization, translation, coding, evidence review, and reporting.

The responsible use of AI in evaluation is not about automating judgment. It is about strengthening the evaluator’s ability to work with evidence, identify patterns, communicate clearly, and support better learning and decision-making.