Why study this course?
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This micro-credential introduces generative AI and large language models (LLMs), with a particular focus on tools like Microsoft 365 Copilot.
You'll learn how these systems generate outputs, how prompt design shapes performance, and how multi-step prompt pipelines can help you use AI more effectively at work and in academic settings.
Developed in collaboration with Scottish Government, this course combines clear conceptual explanations with visualisations, example Python code, and hands-on prompt-based activities. It is designed to work for both technical and non-technical learners.
A key focus is informed, critical use. You'll learn to evaluate AI-generated outputs, spot hallucinations and other reliability issues, and judge when human review is needed. The aim is to build your confidence, practical skill, and responsible judgement in using generative AI.
No technical background is assumed for assessment. Code and technical explanations are included as non-assessable material for learners who want to go deeper.
Through this course you will:
- Engage in work-relevant learning through case studies, policy links, and practitioner interviews.
- Build in-demand knowledge and skills, backed by rigorous quality assurance.
- Develop the ability to adapt and keep learning as AI evolves.
By the end you'll be able to:
- Explain and critically evaluate how generative AI and LLMs work.
- Apply technical concepts using example Python code and visual explanations, appropriate to a professional context.
- Design and implement high-quality prompts and multi-step prompt pipelines for real-world tasks in your own workplace.
- Critically evaluate the quality, reliability, and limitations of AI-generated outputs, including hallucinations, inconsistency, and unsupported claims.
- Develop original, creative solutions by integrating AI tools into real workflows, justifying your choices with evidence and reasoning.
- Communicate complex ideas about AI, prompt design, and evaluation clearly to technical and non-technical audiences alike.
- Take ownership of a reflective mini-project on generative AI, considering the ethical, organisational, and accountability issues involved in your own practice.
Credits and progression
10 credits at SCQF level 11 (St Andrews level: 5000).
Credits attained are eligible for stacking towards a new Postgraduate Certificate in Artificial Intelligence.
Assessment
Students must complete a Generative AI Reflective Mini-project and pass an end-of-module quiz. The module grade will be derived from the quiz, but students must pass both elements of the assessment.
Preparation
No coding experience needed. Some familiarity with AI tools such as Copilot is beneficial but not required.
Course details
Course leader
Professor Tom Kelsey FRSM is a Professor of Health Data Science at the University of St Andrews. His work sits at the intersection of medicine, data science and artificial intelligence. Professor Kelsey’s research spans fertility preservation, reproductive endocrinology, and health data science, with a sustained focus on applying computational methods to clinically important problems. Across his publication record, he has contributed to AI search, web-scale distributed AI search across heterogeneous infrastructures, and a broad range of machine learning and statistical modelling studies in biomedical research.
More recently, his work has increasingly centred on AI in medicine, including large language models, generative AI, and explainable AI for clinical decision support and patient-facing applications. His publications include studies on the use of generative AI for follicle growth monitoring, assessments of locally run and web-based large language models in otorhinolaryngology, and AI-based approaches to personalise assisted reproductive technology and improve clinical outcomes. This combination of methodological depth and translational focus has positioned him as an active contributor to the responsible development and evaluation of AI use in medicine.
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Introduction to generative AI, large language models, tokens, training data, inference, and the basic architecture of systems such as Copilot.
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Core ideas including embeddings, encoder-decoder architecture, attention, context windows, sampling, and output generation.
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Principles of effective prompting, including clarity, constraint-setting, role assignment, examples, and iterative refinement.
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Multi-step prompting, task chaining, structured workflows, and the use of generative systems for drafting, planning, summarising, and revising.
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Human assessment of AI-generated text, including accuracy, coherence, relevance, completeness, tone, and transparency.
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Responsible use of generative AI, including verification, disclosure, oversight, and appropriate limits on automation.
What it will lead to
AI is transforming how public services and businesses operate, but the critical, ethical skills to govern it responsibly haven't kept pace. The Scottish Government has identified this gap directly: Skills Development Scotland's Digital Economy Skills Action Plan, the Digital Strategy for Scotland, and the Delivery Plan for Sustainable Digital Public Services (2025-28) all point to the need for people who can think critically and ethically about AI and data, not just use it.
This course puts you ahead of that curve. Whether you're in Scottish Government or another sector, now is the moment to build the skills that national policy says are essential and to put yourself at the front of a fast-moving conversation about how AI should be governed.
Why St Andrews?
When you study with the University of St Andrews, the top university in the UK and Scotland according to The Times and Sunday Times University Guide 2027, you'll join a global community of learners as you explore the complex issues of AI governance.
This expert-led online course, taught by leading academics, provides the same rigorous learning experience as our in-person courses, with the flexibility to study at your own pace.
With dedicated support throughout your studies, you'll gain the insights needed to navigate today's challenges from the comfort of your own home.
Designed for you
Wherever you are, you can take St Andrews with you. Online courses at the University of St Andrews combine all the benefits of studying at one of the world's oldest and best universities with all the advantages of flexible, personalised learning.
The development of this course was funded through the Micro-credentials Pilot for Scottish Government Civil Servants Project (2026).
Entry requirements
No formal entry requirements. Some familiarity with AI tools such as Copilot is beneficial but not required.
English proficiency
This course has no formal entry requirements but is designed for applicants who have a good level of English. You will be asked to self-rate your English language proficiency against the standard undergraduate English language entry requirements. You should have at least a low C1 CEFR score overall, with at least a high B2 level in each of the four skills of reading, writing, listening and speaking.
Fees and funding
£800
Through support from Upskilling Scotland, we have up to 25 fully funded places available for this micro-credential.
You can view eligibility criteria during your application.
Contact us
The School of Computer Science
University of St Andrews
Jack Cole Building
North Haugh
St Andrews
KY16 9SX
Email: admin-cs@st-andrews.ac.uk
Phone: +44 (0)1334 46 3253
Website: The School of Computer Science
Legal notices
Admission to the University of St Andrews is governed by our Admissions policy
Information about all programmes from previous years of entry can be found in the course archive.
Curriculum development
As a research intensive institution, the University ensures that its teaching references the research interests of its staff, which may change from time to time. As a result, programmes are regularly reviewed with the aim of enhancing students' learning experience. Our approach to course revision is described online.
Tuition fees
The University will clarify compulsory fees and charges it requires any student to pay at the time of offer. The offer will also clarify conditions for any variation of fees. The University’s approach to fee setting is described online.
Page last updated: 12 October 2026