MSc Human Centred Artificial Intelligence
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | Applicants are required to have either a 2:1 in a non-related science undergraduate degree, or a 2:1 in any other degree subject and A Level Mathematics at Grade A, or equivalent. |
|---|---|
Why study MSc Human Centred Artificial Intelligence at Exeter?
- Learn how to design AI systems that work with people to solve a range of problems.
- Acquire the technical and design skills needed to co-create AI systems that work with people in different sectors and businesses including in health and environment.
- Understand how AI systems can align objectives with societal values and human needs.
- Develop a unique blend of technical and ethical, social, organisational and psychological skills and knowledge.
- A truly interdisciplinary degree encouraging diverse collaboration and investigation to drive innovation and rapidly expand knowledge in a crucial field.
Fast Track (current Exeter students)
Contact
Programme Director: Professor Andrew Howes
Web: Enquire online
Phone: +44 (0)1392 72 72 72
Top 20 for Computer Science
20th in The Times and The Sunday Times Good University Guide 2024
Top 10 in the UK for graduate prospects
Joint 9th for graduate prospects for Computer Science in the Complete University Guide 2027
Long-established partnership with the Alan Turing Institute and home to the Institute of Data Science and Artificial Intelligence
No programming experience required
Entry requirements
Applicants are required to have either a 2:1 in a non-related science undergraduate degree, or a 2:1 in any other degree subject and A Level Mathematics at Grade A, or equivalent.
Please note, this is a conversion course.
If you have studied Computer Science, Mathematics, Physics, Engineering or other related degree, we very strongly advise that you apply for one of our advanced programmes, please see MSc Advanced Computer Science, MSc Advanced Data Science, MSc Generative Artificial Intelligence or MSc Advanced Computer Science with Business. MSc Human Centred Artificial Intelligence is a conversion course intended for students who have a non-related degree background therefore the course content has been designed so that it is appropriate for that entry level.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
Please visit our entry requirements section for equivalencies from your country and further information on English language requirements.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course.
The required IELTS test scores for this course fall under Profile B1.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Course content
By joining our innovative Masters in Human-Centred AI, you will discover how to address the pressing problem of incorporating human-centred design principles into AI practice. You will learn how to address the ethical imperative of ensuring AI systems align with societal values and cater to human needs. By emphasising the integration of human factors into AI development, you will gain not only technical prowess but also a deep understanding of the ethical, social, organisational and psychological dimensions of AI.
The skills you learn are interdisciplinary, covering AI ethics, human-computer interaction, artificial intelligence, machine learning and their applications, to problems of working with humans in areas such as environmental intelligence, health informatics and wellbeing.
The programme is supported by ongoing research endeavours aimed at ensuring AI systems are developed and deployed responsibly. Moreover, its interdisciplinary nature fosters collaboration across various research domains, driving innovation and advancing knowledge in the field of AI.
The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. Because AI is such a rapidly changing field, the precise modules available to you in future years will vary depending to accommodate cutting-edge research and techniques, staff availability, timetabling and student demand.
Please note that the module information displayed here is subject to change.
150 credits of compulsory modules, 30 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Programming with Python | 15 | |
| Foundations of Human-Centred AI | 15 | |
| Design Methods for Human-Centred AI | 15 | |
| Research Project | 60 | |
| Machine Learning | 15 | |
| Introduction to Data Science | 15 | |
| Learning from Data | 15 | |
COMM109: Programming with Python
This module will introduce students to the fundamentals of constructing software using the Python programming language. You will learn how to decompose problems into components that can be implemented to provide a software solution, as well as how to control program flow and represent data within software. Having learned the fundamentals of Python coding you will be introduced to exception handling, Python classes, and be introduced to principles of software development and testing.
COMM111: Foundations of Human-Centred AI
You will study foundational concepts in how to design Artificial Intelligence (AI) systems that interact with humans. This will involve learning about human psychology including computational theories of how people represent and process knowledge, learn and work together. You will learn about topics including, how people make decisions, how they perform perceptual/manual tasks, how human vision works. You will use these theories to build and critically evaluate Artificial Intelligence systems that work with people.
You should take this module if you are interested in going on to a masters/research degree and/or in the rapidly expanding number of career pathways that involve designing AI to work with people. For these careers learning about human psychology is vital to designing systems that, for example, people find useful but not controlling and people find engaging but not addictive. For example, answers to the following questions require an understanding of the psychology of the user. How can AI be fine-tuned to human preferences and emotions? How can an AI system learn about an individual person’s goals and preferences? How can it learn about their emotions and feelings about others? Answers to these questions can help improve AI systems that work with people in the workplace and the home.
COMM112: Design Methods for Human-Centred AI
Learn the skills needed to practice Human-centred design of Artificially Intelligent systems. You will learn how to use computational design thinking to empathise with people, ideate, prototype and evaluate AI systems. You will learn how to abstract AI problems by engaging with people, communities and contexts. You will apply methods from Human-Computer Interaction to engage with users through participatory design practices.
Having used these methods to abstract Human Centred AI problems, you will learn how to investigate prototype solutions and critically analyse their strengths and weaknesses, both from a computational perspective and a human perspective.
You will attend a weekly class in which an expert in Human-centred AI will lead discussion of an aspect of Human Centred AI design and its implications for how relevant Artificial Intelligence technologies are likely to impact people.
This course is a hands-on, practice-oriented approach to learning Human-centred AI (HCAI) design, with a strong emphasis on the evaluation of AI systems from both technical and human perspectives. Methods covered will include design thinking, participatory design, A/B testing, think-aloud protocols, diary studies, eye tracking studies etc. These tools provide students with the skills required to work with people to understand their needs and desires, understand how and why they perform tasks as they do and design AI systems that work with and for them.
COMM514: Research Project
In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.
Research topics can be selected from across the breadth of computer science, data science and related topics. The project may include theoretical analysis, as well as practical software implementation.
This module aims to give you in-depth experience of research in an area relating to your programme of study. It will help you prepare for projects both in an industry or commercial setting, as well as in further postgraduate research work, such as a PhD. The module builds on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis and presentation of results.
ECMM422: Machine Learning
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.
In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.
ECMM443: Introduction to Data Science
In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, statistical modelling, network and text data analysis. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises and individual study will consolidate your learning and provide the foundations for later study.
This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing students with the tools to formulate data science problems and construct pipelines to begin to solve them technically.
Lectures will be accompanied by data analysis exercises. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation.
ECMM445: Learning from Data
Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.
The module will also cover some recent neural network architectures and related learning algorithms.
This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Leadership and Global Challenges | 15 | |
| Digital Transformation | 15 | |
| Social Networks and Text Analysis | 15 | |
| Stochastic Processes | 15 | |
| Data Governance and Ethics | 15 | |
BEMM071: Leadership and Global Challenges
The aim of this module is to provide you with an opportunity to actively and critically engage in debates about the role of leadership in major global challenges facing the world today. In doing so, the module looks to challenge your implicit conceptions of leadership and to develop a more sophisticated understanding of real-world leadership and its challenges. Each topic will be explored in depth through engagement with academic literature theory and research, as well as practical case-study perspectives and real-world examples. Particular attention will be given to how each topic plays out at the interaction of leaders, organisations, and society.
- Destructive leadership
- Inclusive leadership
- Responsible leadership
This module equips you with the ability to apply critical thinking and understand how different types of leadership contribute to the problems and solutions to the world's greatest challenges. This ability to ask critical questions is an essential higher order thinking skill that will underpin all future management practice.
BEMM190: Digital Transformation
This module will introduce you to the fundamentals of digital transformation through study of a range of practical examples. Organisations must position themselves for success in the digital era to be sustainable. For new ventures, it means creating structures and working practices appropriate to the dynamic environment. For established organisations, it means transforming existing structures and ways of working to meet current and future needs while continuing to meet the expectations of existing clients, employees and other stakeholders. Consequently, digital transformation activities are becoming increasingly strategic across public, private and third sectors.
You will also assess the implications for career development within these disruptive environments. This means building the digital skills required for success such as effective workplace communications across hybrid locations, the use of collaborative online tools, and the importance of ethical behaviour and wellbeing.
This module will help you to:
ECMM447: Social Networks and Text Analysis
The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.
The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open-source packages.
The module will be taught in weekly lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material.
ECMM450: Stochastic Processes
A stochastic process is one that involves random variables. A large number of practical systems within industry, commerce, finance, biology, nuclear physics and epidemiology can be described as stochastic and analysed using the techniques developed in this module. The systems considered may exist in any one of a finite, or possibly countably infinite, number of states. The state of a system may be examined continuously through time or at fixed and regular intervals of time.
You will study processes whose changes of state through time are governed by probabilistic laws, and you will learn how models of such processes can be applied in practice.
Pre-Requisite Modules:
SOCM033: Data Governance and Ethics
Data science, machine learning, artificial intelligence and 'big data' have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.
Fees
2026/27 entry
UK fees per year:
£14,300 full-time
International fees per year:
£30,300 full-time
Funding
We are proud to offer a range of scholarships to help fund your studies. We constantly add to our scholarship portfolio, many of which are funded by our alumni and supporters.
Our prestigious, merit-based Excel at Exeter scholarship is designed to support international applicants with outstanding academic records. We have awards for undergraduate and postgraduate applicants.
Scholarships
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships. We also provide awards for sport, music and other achievements, as well as regional and partner scholarships with organisations such as Chevening, The Beacon Trust and the British Council. For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
University of Exeter Alumni Scholarship
We are pleased to offer the University of Exeter Alumni Scholarship, a scholarship for University of Exeter alumni beginning a standalone postgraduate programme in 2026/27 with us a scholarship worth 20% of the cost of your first year tuition fees.
Terms and conditions, including deadlines, apply.
Teaching and research
Lovelace computer lab
You will be taught by a range of staff with relevant expertise and knowledge in generative artificial intelligence, Large Language Models (LLMs), and machine leaning. This will include senior academic staff, qualified professional practitioners, demonstrators, technicians and research students. You may also benefit from guest lectures from industry.
Teaching
Teaching is mainly delivered by lectures, workshops and online materials. Each module references core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.
Facilities
We have invested heavily in state-of-the-art teaching facilities for Computer Science and related programmes during 2024. The Lovelace lab seats 120 students and is designed to ensure lines of sight and audio are optimised wherever you are in the room.
The Babbage lab seats 60 and benefits from the same design principles, but also benefits from a breakout space, meaning students can experience both ‘chalk and talk’ teaching of theoretical aspects, and try out these fundamentals in the lab in the same session.
Internationally recognised research
We believe every student benefits from being taught by experts active in research and practice. You will discuss the very latest ideas, research discoveries and new technologies, becoming actively involved in a research project yourself.
Supportive environment
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. We operate an open-door policy, so it’s easy to consult individual members of staff or to fix appointments with them via email. As a friendly group of staff, you’ll get to know us well during your time here.
Assessments
The assessment strategy for each module is explicitly stated in the full module descriptions given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods include essays, closed book tests, exercises in problem-solving, use of the web for tool-based analysis and investigation, mini-projects, extended essays on specialized topics, and individual and group presentations.
Academic support
Teaching staff include scientists and leading academics in the field who work with businesses to solve difficult, real-world problems. The university also has a growing number of apprenticeship programmes whose direct links with industry benefit the department.
Careers
As AI rapidly becomes an intrinsic part of everyday life around the world, we need people who can design, build and manage AI systems that are intuitive, ethical, and complementary to human needs and values. Human Centred AI professionals will help design and adopt AI that is not only technologically advanced, but also aligned with these human and organisational needs, and demand for this is only going to increase.
More generally, Computer Science and its related disciplines are at the forefront of technology and innovation. With connectivity at the heart of society, we need graduates who can understand the latest techniques and technology to negotiate problems. There are huge opportunities for businesses and individuals who can solve these problems using cutting edge technologies such as artificial intelligence, machine learning, data science, high performing computing and cyber security.
A Human Centred AI degree provides you with a strong foundation in Artificial Intelligence, Machine Learning, and Generative AI. These skills are highly sought after in most sectors, including both public and private, and demands is only increasing.
Graduate destinations
Career opportunities are limitless, with computer science and related graduates being found in a variety of sectors, including software engineering, health communications, education, life sciences, finance and manufacturing. This programme is particularly suited to professionals and graduates looking to develop career options or pursue academia.
Dedicated careers support
You will receive support from our dedicated Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in developing your enterprise acumen. Please visit the Career Zone for additional information on their services.
