Artificial Intelligence BSc
AAA – AAB at A level, A-level Mathematics; graduates earn a median £33,500 three years on.
Typical A level offer
AAA – AAB
A-level Mathematics · 85% of entrants came with A levels · 90% stay on after year 1 · Glasgow course page, Discover Uni
Year 13 · applying
What do you need to get in?
AAA – AABtypical A level offer
Typical offer — the grades most applicants are asked for. A contextual offer can be lower for applicants from under-represented backgrounds; none is published for this course.
Glasgow course page, read 19 September 2026 · Discover Uni entry profile
- 1A-level Mathematics
Who actually got in: 85% with A levels, 5% with the IB, 5% via Access courses, 5% with other higher education. 50% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 15 entrants.
UCAS tariff points — a points scale for grades: A* = 56, A = 48, so A*A*A = 160. Entrants’ totals include extra subjects.
Other qualifications2
| Qualification | Typical offer | Required subjects |
|---|---|---|
| Scottish Highers | AAAAAAminimum from S5: AAABB | Higher Mathematics at A (AH recommended) and Computing (if Higher Mathematics A grade is not achieved in S5). |
| IB | 38 (6,6,6) | HL6 Mathematics (Analysis & Approaches). |
Years 1–4
What do you study?
6compulsory modules in year 1
Compulsory modules this year: digital & ict skills 51% · mathematics & statistics 30% · transversal skills 7% · engineering & manufacturing 5%.
Module — one course unit, usually a term or a year long; “core” means everyone takes it. One UK credit is about ten hours of study; a full year is 120 credits.
Glasgow course page · optional modules vary by year
Year 1 · 6 core · 110 credits ≈ 1,100 hours
- Computing Science 1P (Standard Route)
What you learn (14)
- design and implement programs using core programming constructs
- evaluate programs built with fundamental programming constructs
- recognise different programming paradigms
- apply an appropriate programming paradigm to solve a problem
- select suitable algorithms and libraries for a given problem
- apply chosen techniques and algorithms to solve a problem
- analyse code to determine its structure and behaviour
- explain code correctness, efficiency and style
- describe a problem and proposed solution approach clearly
- design a top-level plan for solving a problem
- translate a design plan into working code
- identify different types of program errors
- apply testing strategies to fix program errors
- build robust programs that handle errors effectively
- Computing Science 1F - Computing Fundamentals
What you learn (11)
- explain how applications are structured around databases and large data collections
- describe the processes and operational tasks a dbms performs
- build a relational database from a given design using a dbms product
- write sql statements to create and manipulate a database
- describe issues affecting successful interaction design
- evaluate the usability of interactive systems using informal methods
- determine when and how to apply usability evaluation outcomes
- describe human-computer activities as structured tasks
- analyse the structure and conditions of a task
- apply task analysis within the software engineering process
- discuss the legal and ethical responsibilities of computing professionals
- Computing Science - 1CT Introduction to Computational Thinking
What you learn (13)
- identify key features shared by computational systems
- describe how a given computational concept functions
- compare how the same concept is realised across different programming languages
- explain how a partial computation operates at a given abstraction level
- explain how a complete computation operates at a given abstraction level
- apply computational concepts to solve problems involving text data
- apply computational concepts to solve problems involving numerical data
- apply computational concepts to solve problems involving graphical output
- modify an existing computation to change its behaviour
- identify errors within computations written in different notations
- correct identified errors to restore correct program behaviour
- identify examples of computational concepts applied in artificial systems
- identify examples of computational concepts arising in natural systems
- Computing Science 1PX (Alternate Route)
What you learn (12)
- produce a top-level plan with stepwise refinements for a problem
- convert a structured design into a working program
- apply testing techniques to detect logical errors in code
- correct identified logical errors in a program
- evaluate the complexity of a program's logic
- integrate error handling features into a program
- integrate event handling features into a program
- design custom data structures to solve a given problem
- apply advanced programming techniques such as recursion to solve problems
- identify appropriate standard algorithms such as sorting and searching
- incorporate standard algorithms effectively into problem solutions
- explain a problem and its solution clearly to others
- Computing Science - 1S Systems
What you learn (10)
- explain how data is represented using bits and words
- convert numbers between binary and two's complement representations
- describe the main subsystems of a processor architecture
- write a simple assembly language program
- run an assembly program using appropriate development tools
- describe how machine instructions are encoded and represented
- explain how the CPU executes programs via the fetch-execute cycle
- design simple synchronous logic circuits using gates and flip-flops
- simulate the behaviour of designed logic circuits
- relate processor architecture concepts to high-level programming languages
- Mathematics 1
What you learn (18)
- communicate mathematical ideas using sets, functions, relations and groups
- identify the components of a mathematical statement such as hypotheses and conclusions
- construct valid proofs using direct argument, induction, contradiction or counterexample
- solve equations and inequalities across number systems and geometric settings
- solve differential equations using algebraic and calculus techniques
- define groups and subgroups and recognise their properties
- perform group operations using cycle notation for permutation groups
- calculate the order of groups and elements using Lagrange's theorem
- derive derivatives and integrals from first principles using limits
- relate differentiation and integration through the fundamental theorem of calculus
- compute derivatives of scalar and vector functions using standard rules
- compute integrals using substitution, parts and other standard methods
- apply calculus techniques to solve optimisation and geometric measurement problems
- perform matrix and vector operations in three-dimensional space
- model real-world problems using mathematical tools and interpret the solutions
- extract key concepts and methods from independent reading of mathematical texts
- present mathematical arguments in writing with precise notation and clear conclusions
- collaborate with peers to discuss and solve mathematical problems
Year 2 · 7 core · 30 credits ≈ 300 hours
- Computing Science 1F - Computing Fundamentals
What you learn (11)
- explain how applications are structured around databases and large data collections
- describe the processes and operational tasks a dbms performs
- build a relational database from a given design using a dbms product
- write sql statements to create and manipulate a database
- describe issues affecting successful interaction design
- evaluate the usability of interactive systems using informal methods
- determine when and how to apply usability evaluation outcomes
- describe human-computer activities as structured tasks
- analyse the structure and conditions of a task
- apply task analysis within the software engineering process
- discuss the legal and ethical responsibilities of computing professionals
- Algorithmic Foundations 2
What you learn (12)
- convert everyday statements into predicate logic notation
- convert everyday statements into set theory notation
- convert everyday statements into relational algebra notation
- formulate assertions using predicate logic
- formulate assertions using set theory
- formulate assertions using relational algebra
- construct formal proofs of assertions using logical laws
- describe how inductively-generated structures are formed
- construct proofs using mathematical induction
- apply basic combinatorial techniques to solve counting problems
- explain fundamental concepts of discrete probability theory
- apply discrete probability methods to solve simple problems
- Algorithms & Data Structures 2
What you learn (13)
- explain the role of abstract data types in program design
- apply stacks, queues, lists, sets and maps to suitable problems
- identify appropriate contexts for using particular data structures
- express abstract data types as Java interfaces
- describe the structure and purpose of arrays, linked lists, binary trees and hash tables
- implement fundamental data structures using Java
- map standard abstract data types onto suitable concrete data structures
- apply recursion to design algorithms for suitable problems
- analyse algorithm efficiency using time and space complexity measures
- implement standard searching and sorting algorithms in Java
- evaluate the computational complexity of searching and sorting algorithms
- use Java collection classes along with comparators and iterators
- apply Java's List, Set, Queue and Map collection implementations
Year 3 · 3 core
Year 4 · 2 core · 50 credits ≈ 500 hours
- Individual Project (H) (Single)
What you learn (14)
- organise and manage a large independent project
- select appropriate technologies and tools for a task
- apply chosen technologies and approaches effectively
- combine technical skills to solve a complex problem
- build a substantial software product demonstrating technical competence
- evaluate the results of their own project work
- demonstrate professional conduct and responsibility during project work
- record time spent on project tasks
- use supervisor feedback effectively to guide progress
- write a clear dissertation documenting the project
- justify design and methodological decisions in writing
- situate the project within its wider academic context
- deliver an oral presentation of project work to a technical audience
- produce a concise video summary of project work
- Research Methods And Techniques (M) for MSci
What you learn (8)
- evaluate the quality and validity of research papers
- conduct a thorough search of relevant academic literature
- compose a detailed written survey of existing literature
- design a sound experimental methodology for a computing research question
- draft a persuasive research project proposal
- critique the strengths and weaknesses of experimental designs
- explain how controls and sample size affect experimental validity
- discuss ethical approval requirements for research experiments
Year 5 · 2 core · 60 credits ≈ 600 hours
- MSci Individual Project
What you learn (15)
- define a focused research problem within a specialist field
- justify the relevance and scope of the chosen research problem
- design a feasible approach to address the research problem
- justify the chosen approach against alternatives
- compile a comprehensive survey of literature relevant to the research topic
- critically evaluate key prior work related to the research problem
- develop a structured plan for conducting the research
- conduct a coherent investigation using appropriate computing science methods
- generate valid results from the investigation
- formulate a clear thesis and argument for an academic paper
- describe research methods and results clearly in written form
- evaluate the significance of findings in relation to other research
- prepare visual and technical materials for an academic presentation
- deliver an academic talk presenting research findings
- respond effectively to academic questions on the research
“What you learn” restates each module’s published learning outcomes in our own words, as short skill statements in the same form as the GCSE and A level curriculum, with the help of an AI model; fields of knowledge follow the twelve fields of the knowledge sphere. The university’s module page, linked from each title, is the authoritative description.
After graduating
Where does it lead?
£33,500median pay three years after graduating
Lower quarter£27k
Median£34k
Upper quarter£45k
Median — the middle earner: half earned more, half less. Pay is for graduates of this subject at this university who finished in 2016-18, not adjusted for inflation; based on 85 graduates.
Discover Uni dataset (HESA and Office for Students, CC BY 4.0, contains HESA data); earnings from the Longitudinal Education Outcomes data (Department for Education) · aggregation level 12, 13, 22
Half of graduates earned between £26,500 and £44,500. Five years on, the median is £46,000. 90% were in work or further study 15 months after finishing; 85% of those in work were in professional or managerial jobs.
All outcome figures7
| Measure | Value | Based on |
|---|---|---|
| Continue after year 110% left | 90% | 280 |
| In work or study, 15 months on7% studying | 90% | 70 |
| Professional or managerial job | 85% | 50 |
| Pay 15 months on, median | £35,000 | 35 |
| Pay 3 years on, median | £33,500 | 85 |
| Pay 5 years on, median | £46,000 | 80 |
National Student Survey 2025-26
What do students say?
78%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 67% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 170 students answered
Highest: 92% agreed on question 15; lowest: 50% on question 14.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 91% |
| Question 2 | 76% |
| Question 3 | 91% |
| Question 4 | 78% |
| Question 5 | 78% |
| Question 6 | 84% |
| Question 7 | 77% |
| Question 8 | 76% |
| Question 9 | 81% |
| Question 10 | 66% |
| Question 11 | 88% |
| Question 12 | 76% |
| Question 13 | 59% |
| Question 14 | 50% |
| Question 15 | 92% |
| Question 16 | 90% |
| Question 17 | 73% |
| Question 18 | 74% |
| Question 19 | 84% |
| Question 20 | 79% |
| Question 21 | 89% |
| Question 22 | 87% |
| Question 23 | 81% |
| Question 24 | 72% |
| Question 25 | 78% |
| Question 26 | 78% |
Question texts are published by the Office for Students; the dataset carries only the question numbers.
Modules, entry requirements and fees are facts read from University of Glasgow’s course page on 2026-09-19 and may change; the university’s page is authoritative. Descriptions are not reproduced. Outcome figures are from the Discover Uni dataset. University of Glasgow is a trade mark of the university; sch.today is independent of it.