AI Use in Evaluation in the UK: Case Study
Responsible AI Use in Evaluation in the UK: A Practical Case Study for Evaluators
An EvalCommunity case study for evaluators, M&E specialists, commissioners, researchers, consultants, and development practitioners applying AI responsibly across the evaluation lifecycle.
10 min read
Editorial Note
This case study is an EvalCommunity educational interpretation of the UK Evaluation Society guidance. The opinions, framing, examples, and practical lessons in this article are EvalCommunity’s own and should not be read as the official position or opinion of the UK Evaluation Society.
Introduction
Responsible AI use in evaluation in the UK means applying artificial intelligence tools in ways that protect evaluation quality, human judgement, transparency, accountability, stakeholder trust, data protection, confidentiality, and professional credibility. AI can support evaluation design, methodology development, data analysis, evidence synthesis, and accessible reporting, but it should not replace the evaluator’s responsibility for evidence, interpretation, and recommendations.
This EvalCommunity case study is based on AI in Evaluation: Good Practice Guidelines for Practitioners, published by the UK Evaluation Society in November 2025. The guidance was co-produced by the UK Evaluation Society AI Working Group and provides voluntary professional guidance for responsible AI adoption in evaluation practice.
The guidance applies across the evaluation lifecycle, including design and planning, data collection, analysis and synthesis, and reporting and dissemination. It is tool-agnostic, covering both publicly available AI platforms, such as large language models, and bespoke AI solutions developed for specific organisations or evaluation purposes.
Quick Answer
AI can support evaluation work when its use is transparent, proportionate, human-led, risk-managed, and verified. Evaluators should document AI use, disclose it appropriately, assess risks, protect participants, and verify AI-generated outputs before using them in evaluation findings.
Download the UK Guidance
Download the UK Evaluation Society guidance that informed this EvalCommunity educational case study.
Key Takeaways
- AI should support evaluation quality and purpose, not drive methods simply because the technology is available.
- Evaluators retain full responsibility for methodology, analysis, findings, recommendations, and final outputs.
- AI use should be transparent to clients, stakeholders, and report users through proportionate disclosure.
- Risk assessment should cover bias, discrimination, privacy, stakeholder trust, environmental impact, and evaluation credibility.
- AI-generated content must be verified before being used in evaluation work.
- Contracts and terms of reference should define permitted AI uses, restrictions, data protection duties, and verification responsibilities.
- Lower-risk, medium-risk, and higher-risk AI applications require different safeguards.
Table of Contents
Case Background
The UK Evaluation Society guidance recognises that AI offers significant opportunities to enhance evaluation practice across the evaluation lifecycle. These opportunities include supporting evaluation design, methodology development, data analysis, evidence synthesis, and accessible reporting.
At the same time, the guidance emphasises that evaluation credibility depends on rigorous standards of evidence, transparency, and accountability that serve decision-makers and communities effectively. AI adoption decisions should therefore be driven by evaluation quality and purpose, not by technological capability alone.
The guidance is intended for evaluation practitioners using or considering AI tools in their work. It is relevant to individual consultants, small NGOs, large government departments, healthcare organisations, academic institutions, private sector evaluators, commissioners, researchers, and practitioners across different organisational contexts.
Scope and Application
For EvalCommunity users, one of the most important points in the guidance is that AI use is not limited to one stage of an evaluation. It may appear during design and planning, data collection, analysis and synthesis, and reporting and dissemination.
The guidance also makes clear that these principles should complement existing evaluation standards and organisational policies. They should be applied alongside policies on data protection, information security, ethics, safeguarding, procurement, and professional conduct. Where there is a conflict, established evaluation standards and regulatory requirements should take precedence.
Evaluation lifecycle uses of AI
- Design and planning: refining evaluation questions, mapping stakeholders, drafting tools, and reviewing background documents.
- Data collection: supporting topic guide development, accessibility adaptations, and communication materials.
- Analysis and synthesis: assisting with document synthesis, coding support, pattern identification, or evidence mapping.
- Reporting and dissemination: drafting plain-language summaries, structuring reports, creating accessible versions, and checking consistency.
The Case Scenario
A small evaluation team is commissioned to evaluate a national community skills programme in the UK. The programme supports adults facing barriers to employment through training, mentoring, and local employer engagement. The evaluation includes document review, stakeholder interviews, survey analysis, and a final learning report for commissioners and delivery partners.
The team faces a tight deadline and a large volume of evidence. They are considering AI tools to support document synthesis, draft report structuring, plain-language summaries, and initial organisation of qualitative material. However, the evaluation also includes sensitive participant experiences, including unemployment, financial insecurity, disability, and confidence barriers.
The team’s key decision is not simply whether AI can be used. The real question is whether AI can be used responsibly, transparently, and proportionately while maintaining evaluation quality, participant protection, stakeholder confidence, and professional accountability.
Applying the Four Core Principles
Principle 1: Transparency, accountability, and competence
The team decides that AI use must be documented and explainable. They create an AI use log recording the tool used, the evaluation task supported, the type of information entered, the output generated, the verification method, and any known limitations.
They also agree that AI will not be used for any task unless at least one evaluator understands the tool’s basic capabilities, limitations, and risks well enough to explain the use to the commissioner and evaluation stakeholders. Where tool information is incomplete, they document uncertainty and seek technical input if the gap creates unacceptable risk.
Principle 2: Human control and proportionate AI use
The evaluation team keeps all critical decisions human-led. AI may support summarising non-sensitive documents, drafting plain-language text, and organising background materials, but it cannot make evaluative judgements, determine findings, or write recommendations without human analysis.
The team decides that AI use must be proportionate to the task. Basic formatting or administrative support requires lighter safeguards, while any AI use connected to analysis, evidence synthesis, stakeholder communication, or emerging conclusions requires stronger review and documentation.
Principle 3: Active risk management and harm prevention
Before using AI, the team identifies risks to participant privacy, stakeholder trust, evaluation credibility, and fair representation of vulnerable groups. They decide not to enter identifiable participant data or sensitive interview transcripts into public AI platforms.
Private, audited, or on-premise AI solutions may be acceptable with additional safeguards, clear data protection controls, and proportionate verification. Any AI use must also comply with applicable data protection requirements, including UK GDPR where personal data is involved.
The team also considers environmental impact. They avoid unnecessary AI processing, choose lightweight uses where possible, and do not use AI for tasks where human review would be simpler, safer, and proportionate.
Principle 4: Quality assurance and verification
The team treats AI outputs as draft inputs, not as evidence. Every AI-supported output must be checked before it informs the evaluation report. Document summaries are verified against original documents. Draft findings are checked against the evidence matrix. Plain-language summaries are reviewed for accuracy, accessibility, and tone.
During testing, the team notices that one AI-generated summary gives less attention to the experience of disabled participants than the original interview evidence. Human review identifies the omission, corrects the summary, and records the issue in the AI use log. This reinforces why AI outputs should be checked against source evidence rather than accepted at face value.
Where full manual checking is not practical, the team uses sampling-based verification and cross-validation with alternative sources. However, any claim used in the final report must be traceable to underlying evidence.
Risk-Based Implementation Framework
The guidance recommends a risk-based approach before implementing AI in evaluation. The central assessment question is whether applying responsible AI principles can sufficiently mitigate identified risks for the intended evaluation purpose.
Risk classification used in the case
- Lower-risk AI use: administrative support, formatting, basic document organisation, and simple summaries of non-sensitive materials.
- Medium-risk AI use: document synthesis, draft stakeholder communication, support for evidence mapping, and preliminary analysis support.
- Higher-risk AI use: processing sensitive data, influencing findings, analysing vulnerable populations, generating recommendations, or shaping conclusions.
In this case, the team allows lower-risk AI use with basic verification, medium-risk use with systematic verification and disclosure, and higher-risk use only if there are enhanced safeguards, secure systems, expert review, and intensive human oversight. Some higher-risk uses are rejected entirely.
Contractual Considerations
The guidance recommends that organisations may wish to address AI use in contractual arrangements. In the case scenario, the commissioner and evaluation team add a short AI clause to the evaluation terms of reference.
Contract points to clarify
- Which AI applications are permitted and which are restricted.
- Whether personal or sensitive data may be processed using AI tools.
- Who is responsible for verifying AI-supported outputs.
- What AI use must be disclosed in proposals, reports, or stakeholder communications.
- How the evaluation team will comply with data protection, confidentiality, and information security requirements.
- How subcontractors and team members will follow consistent AI-use standards.
Sample AI Use Log
A simple AI use log helps evaluators document transparency, accountability, verification, and proportionality. The table below is a practical template for adapting the case study to real evaluation assignments.
| AI-supported task | Risk level | Data entered | Verification method | Decision |
|---|---|---|---|---|
| Summarising public programme documents | Lower to medium | Public or non-sensitive documents | Human review against source documents | Allowed with verification |
| Drafting plain-language summaries | Medium | Human-approved findings and report sections | Check against final evidence matrix | Allowed with human editing |
| Supporting qualitative coding comparison | Medium to high | Anonymised excerpts only, where permitted | Compare with human coding and review discrepancies | Allowed only with safeguards |
| Processing sensitive interview transcripts | Higher | Identifiable or sensitive participant data | Not applicable for public AI tools | Not allowed in public AI platforms |
Documentation and Disclosure
The guidance provides examples of AI disclosure for evaluation reports, proposals, sensitive evaluations, and risk-proportionate use. EvalCommunity users can adapt these examples to their own organisational context, while avoiding generic disclosure that does not explain what AI actually did.
Standard disclosure example
This evaluation used AI tools to support document synthesis, report structuring, and plain-language drafting. All AI-generated outputs were verified through human review, comparison with source materials, and cross-checking against the evaluation evidence matrix. The evaluation team retained full responsibility for methodology, analysis, findings, conclusions, and recommendations. AI use was conducted in line with relevant ethical principles, data protection requirements, and professional evaluation standards.
Minimal disclosure for lower-risk AI use
This evaluation used AI tools for basic administrative and drafting support. All outputs were reviewed by the evaluation team, and the team retained full responsibility for the quality and accuracy of the final evaluation outputs.
Enhanced disclosure for sensitive evaluations
This evaluation used AI tools for specified support tasks with enhanced safeguards due to the sensitive nature of the data and populations involved. Additional protections included data minimisation, human oversight, bias checks, restricted data entry, and verification against primary evidence. The evaluation team maintained direct accountability for ensuring AI use did not compromise participant privacy, introduce bias, or affect evaluation credibility.
Professional Learning and Capacity Building
The guidance emphasises that responsible AI adoption may require professional development. In this case, the team identifies several learning needs before using AI more extensively.
- Understanding AI capabilities and limitations relevant to evaluation applications.
- Developing skills in AI tool assessment, including bias detection and reliability evaluation.
- Learning appropriate verification and validation approaches for different AI applications.
- Building knowledge of data protection and ethical requirements for AI use.
- Engaging specialist expertise through collaboration and professional networks.
The guidance also encourages the evaluation community to share experiences, develop case studies, contribute to refinement of professional guidance, and participate in cross-sector learning about responsible AI use in research and evaluation.
Lessons for EvalCommunity Users
- AI should support evaluation purpose: Use AI only where it improves evaluation quality, efficiency, accessibility, or learning.
- Human judgement remains central: Evaluators must retain responsibility for methods, interpretation, findings, and recommendations.
- Disclosure protects trust: Clients and stakeholders should understand how AI was used and how outputs were verified.
- Verification is essential: AI outputs should be checked before they influence evaluation products.
- Contracts should be explicit: AI use should be addressed in expectations, roles, restrictions, and quality assurance requirements.
- Risk determines safeguards: Higher-risk AI use requires stronger controls or should be avoided.
Download the Source Guidance
Download the UK Evaluation Society guidance that informed this EvalCommunity educational case study.
FAQ
What is responsible AI use in evaluation in the UK?
Responsible AI use in evaluation in the UK means using AI tools transparently, proportionately, and with human oversight. It requires risk management, disclosure, verification, data protection, and full evaluator accountability for final outputs.
Can AI replace evaluators?
No. In this EvalCommunity interpretation of the guidance, AI may support specific evaluation tasks, but it should not replace professional judgement, methodological decisions, interpretation, findings, or recommendations.
When should evaluators avoid AI use?
Evaluators should avoid AI use when risks cannot be mitigated to an acceptable level. This may include cases involving sensitive data, vulnerable populations, weak verification capacity, unclear tool limitations, or unacceptable risks to evaluation credibility.
What should be disclosed about AI use in an evaluation report?
Disclosure should explain what AI was used for, how outputs were verified, what limitations were identified, and that the evaluation team retained responsibility for the methodology, analysis, findings, and recommendations.
How should AI-generated outputs be verified?
Verification should match the role of AI in the evaluation. Methods may include human review, source checking, cross-validation with other evidence, expert review, sampling-based checks, and quality assurance against the evidence matrix.
Should AI use be included in evaluation contracts?
Yes, where relevant. Contracts or terms of reference can clarify permitted AI uses, restrictions, ethical obligations, verification responsibilities, data protection requirements, and expectations for all team members and subcontractors.
Is this case study the official opinion of the UK Evaluation Society?
No. This case study is EvalCommunity’s educational interpretation for practitioners. It is based on the UK Evaluation Society guidance, but the examples, opinions, and framing are EvalCommunity’s own.
Conclusion
This case study shows how evaluators can apply responsible AI principles in practical evaluation work in the UK. AI can support document synthesis, reporting, communication, accessibility, and evidence organisation, but only when its use is transparent, proportionate, human-led, risk-managed, and verified.
For EvalCommunity users, the central lesson is clear: AI should strengthen evaluation quality, not weaken professional judgement or public trust. Responsible AI use in evaluation requires clear documentation, stakeholder transparency, risk-based safeguards, contractual clarity, professional competence, and a firm commitment to human accountability.
