United Kingdom · 2026-27
University of Oxford
27 lab practicals for the Oxford syllabus.
University of Oxford Computer Science course synopses for 2026-27: databases, database systems implementation and AI. Every lesson is stepped, compiled and checked by a real toolchain on your own machine — the model reviews your code afterwards, it does not decide whether you passed.
Updated
Semester by semester
Programme: BA / MCompSci Computer Science. Scheme: 2026-27 course synopses. Open a course to see its practical units and the lab lesson GroutCode ships for each.
Part A
2 courses20 lab lessonsJavaSQL- AIJava
Artificial Intelligence
10
lessons
- ELIZA, the Turing test and a rational vacuum agent
- Uninformed search on the map of Romania
- 1ELIZA, the Turing test and a rational vacuum agentUnit 1 · Introduction to Artificial Intelligence
- 2Uninformed search on the map of RomaniaUnit 2 · Problem solving and search
- 3Heuristics and A* — the 8-puzzle and the road to BucharestUnit 3 · Informed search
- 4Local search — hill climbing, annealing and genetic operators on n-queensUnit 4 · Local search
- 5STRIPS planning — progression, regression and the Sussman anomalyUnit 5 · Planning
- 6Robot motion planning — C-space, Dijkstra, Voronoi skeletons and potential fieldsUnit 6 · Dealing with geometry of physical agents
- 7Modelling CSPs — constraint graphs, the SAT reduction and quantified CSPsUnit 7 · Constraint satisfaction problems (CSPs)
- 8CSP solvers — backtracking, MRV, forward checking, AC-3 and tree CSPsUnit 8 · Solving CSPs
- 9Game-playing agents — minimax, alpha-beta and expectiminimax on tic-tac-toeUnit 9 · Playing games
- 10Beyond classical search — AND-OR plans, belief states and online agentsUnit 10 · Beyond classical search
- DBSQL
Databases
10
lessons
- From an E/R diagram to tables: designing a college library database
- The relational model: domains, keys, referential actions and translating ISA
- 1From an E/R diagram to tables: designing a college library databaseUnit 1 · Introduction, E/R diagrams
- 2The relational model: domains, keys, referential actions and translating ISAUnit 2 · Relational model, E/R to relational
- 3Relational algebra and calculus in SQL: from selection to divisionUnit 3 · Relational algebra and Calculus
- 4An overview of SQL on an exam-marks databaseUnit 4 · Overview of SQL
- 5Dependencies and normal forms: testing FDs and decomposing to 3NF and 4NFUnit 5 · Schemas, dependencies and normal forms
- 6Atomic bank transfers and a schedule analyser in SQLiteUnit 6 · Transaction Management
- 7Indexes, pages and hash buckets: storage in SQLiteUnit 7 · Storage and indexing
- 8Walking and validating a B+ tree stored in tablesUnit 8 · Tree Indexes
- 9A page-I/O cost model for scans, sorts, selections and joinsUnit 9 · Query evaluation
- 10A cost-based join optimiser from catalog statisticsUnit 10 · Query optimisation
Part C
1 course7 lab lessonsC++- DSIC++
Database Systems Implementation
7
lessons
- Disk access costs and the I/O model — pricing every block a DBMS reads
- Slotted pages, a buffer pool and file layouts — the storage layer of a mini-DBMS
- 1Disk access costs and the I/O model — pricing every block a DBMS readsUnit 1 · Hardware
- 2Slotted pages, a buffer pool and file layouts — the storage layer of a mini-DBMSUnit 2 · File and System Structure
- 3Hash indexes, a B+ tree and a k-d tree — building the index structures of a DBMSUnit 3 · Indexes
- 4External merge sort — sorting files far bigger than memoryUnit 4 · External Sorting
- 5Relational operators — selection, projection, four joins and a pipelined executorUnit 5 · Query Evaluation
- 6A cost-based query optimizer: histograms, join costs and join orderingUnit 6 · Query Optimization
- 7Concurrency control: serializability, two-phase locking and deadlocksUnit 7 · Transaction Management
The full 42-course syllabus is transcribed in the app, including the theory courses. Lessons are authored against the course codes that have practical work, and the list grows with each release.
GroutCode is not affiliated with or endorsed by University of Oxford. Course codes and titles are transcribed from the institution’s published scheme documents.
How a practical runs
The same five steps for every lesson, in every language.
- 1
Pick your syllabus
Choose your university or board and the semester or class you are in. The course list is the transcribed official scheme, not an approximation.
- 2
Open a lab lesson
Each lesson is a stepped practical for a specific course, with a starter file you edit and a test file you do not.
- 3
Write the code yourself
The starter has the signatures and the TODOs. You implement them in the editor, in the language your course actually uses.
- 4
Run the checks
The toolchain compiles and runs the tests — 8 to 15 of them across the lesson, one per step, so you find out which change broke what.
- 5
Get the review
Once the tests pass, the model reads your implementation and tells you the input that breaks it. It never reports the pass or fail itself.
Why the compiler grades, not the model
The obvious way to build an AI lab tool is to let the model read the student’s code and say whether it is correct. It is also the way that produces a tool nobody can trust. A model will tell you your code works when the compiler says otherwise, and it will fail you for a style it happens to dislike.
So the two jobs are split:
- The model designs the lesson, writes the starter and the tests, and — after the tests pass — reads what you actually wrote and tells you the input that breaks it.
- The toolchain decides whether it compiles and whether each step passes. That verdict is not negotiable and the model never issues it.
And why steps instead of one big task
A 40-to-60 minute practical with a single Run at the end gives a beginner one bit of feedback an hour, and it arrives after every decision has already been made. Each lesson here is 8 to 15 steps, each with its own instruction and its own check, so you find out which change broke what while you still remember making it.
Frequently asked questions
Does GroutCode cover the Oxford 2026-27?
Yes. GroutCode ships the transcribed Oxford 2026-27 for BA / MCompSci Computer Science — 42 courses — with 27 authored lab lessons mapped to specific course codes. The syllabus was transcribed from the official documents, and the source and checksum of each one is recorded in the app.
Which languages do the Oxford practicals use?
Java (10 lessons), SQL (10 lessons), C++ (7 lessons). The language is set per course code rather than guessed from the title, because the same subject is taught in different languages at different institutions.
Does the AI just write the practical for me?
No. The starter file has the signatures and the TODOs; you implement them. The toolchain — not the model — decides whether your code compiles and whether each step passes, so the AI cannot tell you your code works when the compiler disagrees. What it does afterwards is read your implementation and point out the input that breaks it.
Do the practicals work offline?
The lessons, the syllabus and the test runner are bundled in the app and run locally, so the compile-and-check loop works with no connection. The review step at the end uses a model, which can be a local one or a cloud one with your own key.
What do I need installed?
GroutCode detects which toolchains are present on your machine and tells you what is missing and what would fix it. You need the compiler or interpreter for the language your course uses — a C compiler, a JDK, Python, Node or SQLite.
Other syllabuses
Cambridge
University of Cambridge
The Cambridge Computer Science Tripos, Parts IA and IB: algorithms, databases, object-oriented programming, C and C++.
36
lab lessons
Imperial College
Imperial College London
Imperial College London BEng/MEng Computing: computing practicals, algorithms, databases, operating systems and machine learning.
24
lab lessons
UCL
University College London
UCL BSc and MEng Computer Science, Years 1 to 4: programming principles, OOP, algorithms, compilers and systems.
51
lab lessons
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