Core Python concepts
Text, integers, decimal numbers, booleans, variables, operators and execution order.
Code to AI UX syllabus
From your first Python line to an AI UX portfolio prototype.
This is a practical course for UX researchers, UX designers and aspiring AI UX practitioners. It starts without assumed coding knowledge and uses 93 short browser-based steps to connect Python with research evidence, AI evaluation and responsible interaction design.
Stage 1 · Absolute beginner
Understand what the code is doing
Begin with visible, explainable operations: printing, values, variables, input, number conversion, comparisons and decisions. Teaching steps introduce each idea, guided practice changes it, and checkpoints gate progression.
Interaction logic
Read user input, transform values and choose an outcome with if, elif and else.
Debugging habits
Read friendly errors, isolate the failing line and check behaviour rather than copying syntax blindly.
Stage 2 · Python building blocks
Turn separate instructions into a small working system
Work with collections and reusable logic, then combine them into the transparent retrieval function used by the final project.
Lists and loops
Store groups of values, repeat an operation and create summaries without manual repetition.
Functions and records
Name reusable behaviour, pass information into it and model structured research or knowledge records.
Text matching
Normalise text, compare tokens and retrieve the most relevant answer from a small inspectable knowledge base.
Stage 3 · UX research data
Make evidence reproducible
Apply Python to the kinds of decisions UX practitioners already make: cleaning sessions, calculating task measures and checking whether an overall result hides a group-level problem.
Clean study records
Handle missing values, normalise categories and apply the same transparent rule to every record.
Calculate UX measures
Compute success rates, averages, comparisons and interpretable summaries.
Inspect differences
Slice results by user group and treat the numbers as evidence to investigate—not automatic truth.
Stage 4 · AI foundations
Understand enough of the system to test its behaviour
The course uses simple, transparent mechanisms rather than pretending a small browser exercise is a production model. That makes tokens, intent, similarity, retrieval and evaluation inspectable.
Tokens, intent and similarity
See how text can be represented and compared, and why a plausible match may still be wrong.
Retrieval and grounding
Connect an answer to an inspectable source and test missing, ambiguous and conflicting information.
Repeatable evaluation
Turn usefulness, clarity, grounding and recovery into observable checks across realistic cases.
Stage 5 · Responsible AI UX
Design for uncertainty, consequence and human control
Finish with the decisions that separate a convincing demo from responsible human-centred work: confidence, fallback, oversight, user-group effects, limitations and launch evidence.
Confidence and fallback
Route uncertain results to answer, clarify or human handoff according to evidence and consequence.
Oversight and control
Give users meaningful ways to inspect, correct, reject or escalate system output.
Launch judgment
State what the evidence supports, what remains unknown and what must be monitored after release.
Final project
Build an AI UX assistant you can explain
Project Studio turns your audience, problem, knowledge base, safe fallback and test decisions into editable Python. You can run the project in the browser, inspect every rule and download the source for your portfolio.
Is the course accredited?
No. Code to AI UX is an independent practical foundation and does not award academic credit or replace a degree, bootcamp or software-engineering programme.
Who is it best for?
UX researchers and designers who want technical fluency for research data and AI evaluation, plus applicants preparing for postgraduate human-centred AI or AI UX study.
How does access work?
The first three steps are free. Full access costs £49 once and includes all 93 steps, automated checks, account-based cloud progress and Project Studio. There is no subscription.
What do I need?
A modern web browser and enough time to practise. Python runs privately in the page, so no local installation is required.
Try the teaching, practice and checkpoint loop before paying
The free preview is one complete topic, not a marketing video. Run the code and pass the first checkpoint in your browser.