Artificial Intelligence (Conversion) MSc - 2026/7
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Level 7
Final award and programme/pathway title
MSc Artificial Intelligence (Conversion)
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Artificial Intelligence (Conversion) |
| PGCert | Artificial Intelligence (Conversion) |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PFA61053 | 180 credits and 90 ECTS credits |
| Part-time | PFA61054 | 180 credits and 90 ECTS credits |
QAA Subject benchmark statement (if applicable)
Computing (Master)
Other internal and / or external reference points
This programme is subject to approval. This means that it has received initial agreement from the University and is currently undergoing a detailed final approval exercise, through the University's quality assurance processes. These processes are a requirement for all Higher Education Institutions within the UK, to ensure that programmes are of the highest standard. Occasionally there may be instances where the University may delay or not approve the introduction of the programme.
Faculty and Department / School
Institutes - Surrey Institute for People-Centred AI
Programme Leader
SONG Yi-Zhe (CS & EE)
Date of production/revision of spec
15/07/2026
Educational aims of the programme
- Equip graduates from any disciplinary background with practical competence in AI programming, the foundations of machine learning, and responsible AI principles sufficient to apply AI methods in their professional domain.
- Enable graduates to analyse domain-specific challenges through an AI lens, identifying where data-driven and algorithmic approaches can deliver measurable impact in fields such as health, business, law, creative industries, and the life sciences.
- Develop graduates¿ ability to use modern AI tools and frameworks ¿ including large language models, AI code assistants, and applied machine learning workflows ¿ to prototype, evaluate, and apply AI solutions.
- Ensure graduates understand the ethical, legal, and societal dimensions of AI, including the EU AI Act, algorithmic fairness, and people-centred AI design, so they can act as responsible AI practitioners in any sector.
- Provide graduates with a working conceptual grasp of how machine learning and modern AI systems behave, sufficient to understand, critically evaluate, and reason about AI models in their domain.
- Develop graduates¿ critical and analytical powers through an individual AI+X dissertation project that applies AI to a real challenge in their domain, supervised by a jointly-appointed People-Centred AI fellow.
- Provide graduates with a choice of ten AI+X elective modules spanning health, sustainability, psychology, business, robotics, creative industries, cybersecurity, law, life sciences, and space ¿ each co-designed by domain and AI experts.
- Address the national AI skills gap identified by Skills England and DSIT, producing graduates who combine deep domain expertise with practical AI competence ¿ the profile most demanded by UK employers, who report a 42% salary premium for AI-skilled professionals.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Apply Python programming and AI-assisted development tools to build, test, and debug software for data analysis and machine learning tasks. | CP | PGCert, PGDip, MSc | |
| Formulate domain-specific problems as machine learning tasks, selecting appropriate supervised, unsupervised, or deep learning approaches and evaluating their performance. | KCT | PGCert, PGDip, MSc | |
| Reason about the foundational concepts underpinning machine learning models, sufficient to interpret, critically evaluate, and apply them in a chosen domain. | KC | PGCert, PGDip, MSc | |
| Critically assess the ethical, legal, and societal implications of AI systems, including fairness, accountability, the EU AI Act, and people-centred AI principles. | KCT | PGCert, PGDip, MSc | |
| Design and execute an individual AI+X research project that applies AI methods to a real challenge in a chosen domain, demonstrating independent research skills. | CPT | MSc | |
| Demonstrate practical competence with modern AI tools and frameworks including large language models, neural network libraries, and data visualisation tools for domain-specific applications. | CP | PGCert, PGDip, MSc | |
| Evaluate the societal impact of AI deployment in specific domains, applying people-centred design principles to ensure equitable and responsible AI solutions. | KPT | PGCert, PGDip, MSc | |
| Plan and manage the implementation, governance, and responsible deployment of AI systems within organisational and regulatory frameworks. | CPT | PGDip, MSc | |
| Communicate AI concepts, methods, and findings effectively to both technical and non-technical audiences, including through written reports and oral presentations. | KCT | PGCert, PGDip, MSc | |
| Integrate domain expertise with AI methods to generate actionable insights, recognising the strengths and limitations of data-driven approaches in real-world settings. | KP | PGDip, MSc | |
| Select and apply appropriate AI and machine learning methods ¿ from classical algorithms to deep learning ¿ to solve problems across diverse application domains. | KP | PGDip, MSc |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 credits at FHEQ level 7.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
Part-time
This Master's Degree programme is studied part-time over two academic years, consisting of 180 credits at FHEQ level 7.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time) - FHEQ Level 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| COMM080 | PROGRAMMING FOUNDATIONS | Compulsory | 15 | 1 |
| LAWM161 | ETHICS AND REGULATION OF ARTIFICIAL INTELLIGENCE | Compulsory | 15 | 1 |
| MANM519 | TOPICS IN PEOPLE-CENTRED AI | Compulsory | 15 | 1 |
| COMM078 | CYBER SECURITY MANAGEMENT AND GOVERNANCE | Optional | 15 | 2 |
| EEEM073 | AI AND SUSTAINABILITY | Optional | 15 | 2 |
| EEEM004 | 60 CREDIT STANDARD PROJECT | Core | 60 | Year-long |
Module Selection for Year 1 (full-time) - FHEQ Level 7
Four compulsory modules in Semester 1
Four optional AI+X elective modules (from six) in Semester 2
As part of the approval process the following new modules have been developed and will be added to the programme once available:
Machine Learning Foundations
AI for Life Sciences
AI + Creative Industries
AI + Translation
AI and Digital Transformation for Public Health
Year 1 (part-time) - FHEQ Level 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| COMM080 | PROGRAMMING FOUNDATIONS | Compulsory | 15 | 1 |
| LAWM161 | ETHICS AND REGULATION OF ARTIFICIAL INTELLIGENCE | Compulsory | 15 | 1 |
| MANM519 | TOPICS IN PEOPLE-CENTRED AI | Compulsory | 15 | 1 |
Year 2 (part-time) - FHEQ Level 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| LAWM161 | ETHICS AND REGULATION OF ARTIFICIAL INTELLIGENCE | Compulsory | 15 | 1 |
| MANM519 | TOPICS IN PEOPLE-CENTRED AI | Compulsory | 15 | 1 |
| COMM078 | CYBER SECURITY MANAGEMENT AND GOVERNANCE | Optional | 15 | 2 |
| EEEM073 | AI AND SUSTAINABILITY | Optional | 15 | 2 |
| EEEM004 | 60 CREDIT STANDARD PROJECT | Core | 60 | Year-long |
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | Y | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The Surrey Institute for People-Centred AI (PAI) is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital capabilities: The entire programme is a sustained investment in digital capability. Students build fluency in Python, AI-assisted development tools (GitHub Copilot, Claude Code), modern ML frameworks, and data visualisation tools. Every module includes hands-on lab components. Graduates leave as confident, critical users of contemporary AI toolchains.
Employability: The programme directly addresses the UK AI skills gap identified in the 2025 DSIT-commissioned survey (97% of organisations report at least one AI skills gap) and equips graduates for roles where AI professionals command a 42% salary premium. The AI+X structure means every graduate leaves with a domain-specialised AI profile (health, business, law, creative industries, etc.) matched to where their prior experience lies. The dissertation provides a substantial portfolio piece for employers.
Global and cultural capabilities: MANM519 (Topics in People-Centred AI) and LAWM161 (Ethics and Regulation of AI) explicitly engage with global AI governance frameworks, including the EU AI Act, comparative US and Chinese approaches, and cultural dimensions of algorithmic fairness. AI+X electives surface domain-specific cultural considerations (e.g. bias in clinical AI, creative industries and cultural production, AI in legal systems).
Resourcefulness and resilience: Students enter with no prior computing background and progress to an individual AI+X dissertation in twelve months. The programme is explicitly designed to build confidence and self-efficacy in a technical domain, supporting learners from underrepresented routes into AI. The dissertation component develops independent research, problem-solving, and time management.
Sustainability: EEEM073 (AI and Sustainability) directly addresses climate monitoring, environmental sustainability, and resource efficiency through AI. Core modules address the environmental cost of AI itself (training compute, efficient inference). The programme¿s modular architecture is itself a sustainable use of curriculum development resources, supporting efficient reuse of shared foundation modules across the University¿s conversion programmes.
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.