AI in Evaluation: Foundations for Responsible Practice

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Learn AI for evaluation with the UK Evaluation Society's Centre for Professional Evaluation. This foundational course helps you use AI tools confidently and responsibly in your evaluative practice.

Covering practical applications across the evaluation lifecycle, the UK Evaluation Society’s four principles framework, systematic risk assessment, and quality assurance approaches, it builds your technical understanding and professional judgement so you can apply AI with confidence and provide clear evidence of your learning and commitment to recognised professional principles.

The course comprises four modules, each combining a short introductory video with detailed ebook content, embedded self-check questions, and downloadable practical tools. You will also read selected sections of the UK Evaluation Society Guidelines alongside the course materials.

Each module ends with a graded assessment that you must pass before progressing to the next module. Assessments are scenario-based, testing your ability to apply what you have learned to realistic evaluation situations.

Each module also includes an optional discussion space where you can share reflections, questions, and examples from your practice. A course moderator contributes — surfacing points worth thinking through and drawing connections between participants' experiences.

Compared to some of the other AI courses I have looked at/taken this is the one that genuinely adds value and increases my professionalism and practice.

— Roeland Hemsteede
IOD PARC

Who is this course for

This course is for evaluation practitioners who are using, or considering using, AI tools in their practice and want a clear, structured framework to guide their approach. It supports transparent practice and helps demonstrate that work is grounded in a recognised set of principles. Relevant across government, the voluntary sector, consultancy, healthcare, academia, and international development, and accessible to those without prior AI experience.

What you'll learn

You’ll learn how to apply the UK Evaluation Society’s four principles framework for responsible AI in evaluation, using practical tools for risk assessment, quality assurance, disclosure, and communicating with commissioners.

Structured around four modules and 18 practical learning outcomes, this course provides a clear pathway to help you build the foundational knowledge and confidence to use AI thoughtfully, responsibly and effectively in evaluation. Each module combines core concepts with practical guidance, enabling you to apply what you learn directly to your own work.

Four learning modules

What AI tools can realistically do across the evaluation lifecycle. Effective prompting, current limitations, and common misconceptions.

By the end of this module, you will be able to:

Identify evaluation tasks where AI tools offer genuine practical value across the evaluation lifecycle.
Describe current AI capabilities and limitations with reasonable accuracy, avoiding common misconceptions.
Interact effectively with AI tools through well-structured prompts and iterative refinement.
Articulate why a principled approach to AI adoption matters for evaluation credibility.

How to apply the Society's four principles when AI decisions arise in real evaluations. Working out what to disclose to commissioners and stakeholders, where human judgement remains essential, and how proportionate use shifts with context.

By the end of this module, you will be able to:

Explain how the principles framework relates to existing evaluation standards (OECD-DAC, Magenta Book, sectoral frameworks).UK Evaluation Society.
Apply Principle 1 (Transparency, accountability and competence) to determine what to disclose, to whom, and in what form.
Apply Principle 2 (Human control and proportionate AI use) to design genuine oversight and make proportionality decisions.
Communicate about AI use effectively with evaluation commissioners.

How to apply the UK Evaluation Society Risk Assessment Framework to identify, assess, and mitigate the risks AI introduces. Making sound data security decisions and detecting bias in AI-assisted work.

By the end of this module, you will be able to:

Use the Risk Assessment Framework to evaluate a proposed AI application in evaluation.
Identify risks across quality, bias, transparency, confidentiality, and ethical dimensions for a given evaluation context.
Apply the data security decision framework to determine whether specific data can be processed through specific AI tools.
Detect potential bias in AI-generated evaluation outputs using practical checks.
Design proportionate mitigations for identified risks and assess whether residual risk is acceptable.

How to verify AI-assisted work proportionately, using the UK Evaluation Society QA Checklist. Judging how much checking each output needs, and documenting AI use clearly in evaluation reports.

By the end of this module, you will be able to:

Design verification approaches proportionate to the AI application and evaluation stakes.
Apply the QA Checklist to systematically assure the quality of AI-assisted evaluation work.
Determine ‘good enough’ verification in resource-constrained contexts.
Recognise red flags in AI outputs that require additional verification regardless of resource constraints.
Document QA processes in evaluation reports in ways that demonstrate rigour and support accountability.

Course certificate

On completing the course assessments, you will receive a Certificate of Completion from the UK Evaluation Society's Centre for Professional Evaluation, recognising that you have worked through the foundations of applying AI in evaluation in line with the Society's Good Practice Guidelines.

Course features

Pay by card for immediate access, or request an invoice and we'll process your request within two working days. We'll then be in touch with next steps and access details.

The member exclusive pricing is available from 1st August 2026 onwards.
UK Evaluation Society member prices are available only with a valid member coupon code. Enter coupon code at checkout.
Member coupon codes are for personal one-time use and must not be shared.
Member access applies while your UK Evaluation Society membership remains active. If your membership is due to expire during the 12-month course access period, you’ll need to renew to continue accessing the course at the member rate.
Membership and course special offer available from 18 August 2026 until 31 December 2026.
The UK Evaluation Society's Centre for Professional Evaluation, reserves the right to amend or withdraw the offer at any time.

View course terms and conditions

Terrific course. Very well thought through. Well written. Well put together. Participants will come away having learned something.

— Bridget Dillon
Former UK Evaluation Society President

Compared to some of the other AI courses I have looked at/taken this is the one that genuinely adds value and increases my professionalism and practice.

— Roeland Hemsteede
IOD PARC

It is a really great resource and there is so much in there that will be of value across the evaluation community.

— Rachel Lineham
Foreign, Commonwealth & Development Office

Jonathan Kuhn-Patrick

Independent Evaluation Consultant

Jonathan is Chair of the UK Evaluation Society AI in Evaluation Working Group, with over 30 years’ experience in international development evaluation.

A former UK Evaluation Society Trustee, Jonathan led the development of the UK Evaluation Society’s AI in Evaluation Good Practice Guidelines, the first practitioner guidance on AI in evaluation published by any national evaluation society, and designed this course to translate those guidelines into practical tools and frameworks for everyday evaluation practice.

James Goh

CEO, AILYZE (AI Evaluation Tool)

James is Founder and CEO of AILYZE (www.ailyze.com), an online AI evaluation tool for analyzing documents and spreadsheets to support evaluation methodologies like thematic analysis, outcome harvesting, most significant change, etc., and for creating AI avatar interviewers that can conduct semi-structured voice interviews & recognize participants' facial expressions.

James has led numerous AI-powered evaluations, including for global multilateral organizations like UNESCO, UNICEF, UNFPA, and UNHCR. He also advises & supports evaluation networks and associations such as the American Evaluation Association, UK Evaluation Society, New Zealand Association, African Evaluation Association, among others.

He has a master's from MIT and bachelor's degrees from The Wharton School & College of Arts and Sciences at the University of Pennsylvania.

Laura Maio

Senior MEL Advisor

Laura is an evaluator and researcher with 10+ years of experience in the UK, China, Bosnia Herzegovina, Afghanistan, Kenya and Tanzania.

She is passionate about inclusiveness and has worked across the Academia and the Third Sector, both in the UK domestic health sector and International Development, championing qualitative methods.

Cormac Quinn

UK Evaluation Society Trustee

Cormac is a UK Evaluation Society Trustee, as well as Chair of the AI in Evaluation Steering Group. He has spent over 20 years working in international development, including 13 years living and working in Rwanda, Mozambique, Zambia and the Palestinian Territories.

He’s currently the Monitoring, Evaluation and Learning (MEL) lead at the Foreign, Commonwealth and Development Office (FCDO) for its growth agenda. This includes a focus both on economic development in partner countries and support for the UK’s international growth ambitions.
He previously worked as MEL lead for the FCDO Private Sector Department, shaping its work on global financial investment and has held a variety of roles in the former Department for International Development (DFID).

Outside of the UK government, Cormac also spent time working as a consultant for Ecorys, supporting international organisations on MEL, and spent three years at the European Commission in Brussels, reviewing EU country and regional development programmes.

Cormac has a wide range of experience in evaluation techniques (from theory-based approaches to impact evaluations); results monitoring (including development of key performance indicators); statistical approaches (including support for national censuses and surveys); and government administrative data systems. In addition, he has extensive understanding of managing international aid programmes.

Outside of work, Cormac is a father of two active teenagers and can often be seen cheering on his beloved football team Millwall on most Saturdays.

Daisy Smith PhD

Senior Expert, Data Science, Integrity

Daisy Smith is a Senior Data Scientist applying data analytics and AI to evaluation, monitoring and learning in international development and public policy. She has supported evaluation and learning for organisations including the FCDO, World Bank and UN agencies.

Her work combines quantitative and qualitative analysis, machine learning and data engineering to generate robust, evidence-based insights. With AI, she focuses on applications across the evaluation lifecycle while ensuring transparency, human oversight and rigorous quality assurance.

Daisy has led the development of data pipelines and Generative AI approaches for analysis and reporting, and specialises in translating complex methods into practical tools for evaluation teams. As a member of the UKES AI in Evaluation steering group, she brings experience in supporting evaluators to use AI effectively, responsibly and in line with professional standards.

Maria Zhitkov

Principal Analyst UKRI

Maria Zhitkov (née Rotaru) is a Principal Analyst at UK Research and Innovation, specialising in evaluation, policy insight, and strategic analysis. She has extensive experience leading complex evaluations and delivering high-quality analysis across government, regulatory, and research funding environments.

Maria has worked across UK central government, including the Department of Health and Social Care, as well as in international and consultancy roles supporting the UK Government and the European Commission. She has led large-scale programme evaluations, contributed to policy development, and produced influential evidence to inform decision-making at senior levels.

Her expertise includes mixed-methods evaluation, data governance, and stakeholder engagement. She is particularly interested in strengthening the use of evidence in policy, and in advancing evaluation approaches that support learning, accountability, and innovation.

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The Centre for Professional Evaluation is part of the UK Evaluation Society, which has supported evaluators for more than 30 years.

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AI for Evaluation: Foundations

Course overview

Module 1 Capabilities for Evaluation Practice

Module 2 UK Evaluation Society Principles Framework

Module 3 Risk Assessment in Practice

Module 4 Quality Assurance and Verification

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Course Director

Course advisory group