TrainingIn progress
MSc Artificial Intelligence and Creative Practice
Postgraduate study combining machine learning fundamentals with studio-based creative research. Assessed through written analysis and produced artefacts, not exams.
Portfolio section — 2026
AI Career
Retraining Portfolio
Violetta Liszka. A documented return to work after several years outside regular employment, built around AI literacy, postgraduate study and creative practice — and designed from the start for disability-accessible, remote conditions.
- Study
- MSc AI & Creative Practice
- Mode
- Remote, asynchronous
- Focus
- AI-only role readiness
- Evidence
- Artefacts, not claims
Why this project exists
A long gap in employment is usually read as a gap in capability. It rarely is. What it actually removes is recent, verifiable evidence — the thing employers screen on first. This project exists to rebuild that evidence deliberately, in public, at a pace that a variable health condition can sustain.
It is not a retraining scheme narrative. There is no claim that AI makes the gap irrelevant. The claim is narrower and more testable: that a body of dated, documented work — written analysis, produced images, evaluated tools — is a stronger signal than a chronology.
The second reason is fit. AI-adjacent remote work is one of the few areas where asynchronous, written-first output is the norm rather than an accommodation. That makes it worth researching properly rather than assuming.
Training foundation
Applied AIOngoing
Working AI literacy
Daily practice with generative text, image and workflow tools. Emphasis on prompt structure, verification of outputs, and knowing where a model is unreliable.
CreativeContinuous
Visual portfolio practice
Image sequences, layout systems and typographic work produced alongside the technical study, used to test AI tools against a real production standard.
AccessMethod
Disability-accessible working method
Work is structured around variable capacity: asynchronous output, written-first communication, and tasks broken into short, resumable units.
MSc Artificial Intelligence and Creative Practice
Goldsmiths, University of London — School of Computing. A Level 7, 180-credit programme combining artistic practice, critical theory and applied AI methods, delivered entirely online and part time.
The programme treats AI as a creative, cultural and socio-technical system rather than a toolset. It develops critical and collaborative approaches to AI in contemporary practice, and asks students to analyse the conceptual, cultural, ethical and technical dimensions of the systems they work with.
Assessment is portfolio-based and public-facing: eight assessments across coursework, live participation and multi-modal portfolios, with no exams. That structure is the reason it fits this project — every module produces a dated artefact rather than a grade alone.
Delivery is 100% online across 264 directed learning hours, part time over 24 months, with optional live sessions. Graduate routes named by the programme include creative technologist roles in digital agencies, studio practice specialising in creative AI, and the games industry.
- Award
- MSc, FHEQ Level 7
- Credits
- 180 CATS / 90 ECTS
- Institution
- Goldsmiths, University of London
- School
- Computing
- Mode
- Online distance learning — 100% online
- Pace
- Part time, 24 months
- Assessment
- 8 assessments: portfolio, coursework, live — no exams
- Intakes
- January, May, August/September
- Phase 1 · 15 credits
Introduction to Creative AI
Foundational knowledge: AI as a creative, cultural and socio-technical system.
- Phase 1 · 15 credits
Collaborative Prototyping and Practice
Building practice-based AI artefacts through interdisciplinary teamwork.
- Phase 1 · 15 credits
More-Than-Human Creativity: Proposal Development
Framing and proposing original creative-AI research.
- Phase 1 · 15 credits
Creative AI: Intermedial Learning Systems
Applied AI methods across media, with critical evaluation of outputs.
- Phase 2 · 15 credits
Portfolio I: The AI An-Archive
Tools for disrupting, unmaking and reimagining creative assets.
- Phase 2 · 15 credits
Portfolio II: Professional Practice for Hybrid Creators
Critical methods for collaborating with AI systems in professional contexts.
- Phase 2 · 15 credits
Portfolio III: Critical Convergence
A symposium on AI, art and the politics of making; assessed by critical reflection.
- Phase 2 · 15 credits
Portfolio IV: Critical Reflection
Interrogating creative agency in the age of AI; assessed by symposium participation.
- Phase 3 · 60 credits
Major Project
A substantial, public-facing practice-based project drawing on the full programme.
Source: Goldsmiths programme specification, MSc Artificial Intelligence and Creative Practice, effective 2026.
AI Engineer pathway — Robust IT
A structured, certification-led route running alongside the MSc: Microsoft Azure data and AI credentials, taken in sequence rather than as isolated courses.
The pathway covers data fundamentals, Azure data services, AI workloads, data engineering, AI solution development and machine learning, ending in placement support. It is delivered remotely and self-paced, which is why it fits a variable-capacity working method — each certification is a discrete, resumable unit of evidence.
Its value here is not the marketing framing — typical UK AI engineer salaries quoted at £55k–£100k, 12–18 months to qualify — but the fact that Microsoft certifications are externally verifiable. They answer the evidence problem a career gap creates in a way that self-directed study does not.
The MSc supplies the critical and creative frame; the pathway supplies the engineering vocabulary and the credentials employers filter on. They are deliberately paired.
- Step 1
Introduction to Data & Databases
Data fundamentals, database concepts, storage and retrieval, data security basics.
- Step 2
Azure Data Fundamentals (DP-900)
Relational and non-relational data, analytics workloads, core Azure data services.
- Step 3
Introduction to AI in Azure (AI-901)
AI workloads and responsible-AI principles, machine learning, vision and language services.
- Step 4
Microsoft Fabric Data Engineer (DP-700)
Pipelines, transformation and the engineering layer that real AI systems depend on.
- Step 5
Azure AI Engineer Associate (AI-102)
Designing and implementing AI solutions, including language, vision and LLM-backed services.
- Step 6
Azure Data Scientist Associate (DP-100)
Machine learning modelling, experimentation and deployment on Azure.
- Step 7
Career placement and job support
Structured application support at the end of the pathway.
Certificates
Completed, dated and externally issued. Collected as a single downloadable document.
Breakthrough AI Skills Bootcamp
Prompt and context engineering, AI automation and implementation, AI marketing, branding and storytelling, ethics, project management. Funded by the Department for Education. October 2025.
International Symposium on Human-AI Workflows
Attendance certificate — Creativity, Labour & Education. Online, June 2026. Research-level discussion of AI in creative and educational practice.
AI-only roles research
Findings from reading live postings weekly, recording required competencies and discarding promotional language.
AI content and prompt operations
Largely remote and asynchronous. Output is judged on written artefacts, which suits a written-first working method.
Prompt structure, editorial judgement, factual verification, tone control.
Model evaluation and data annotation
Genuinely AI-only and remote. Volume-based, but the higher-paid tiers reward domain reasoning rather than speed.
Consistent rubric application, written rationale, comfort with ambiguity.
AI-assisted design and image production
Hybrid in practice — most postings still expect conventional design competence with AI as an accelerator, not a replacement.
Composition, typography, retouching, art direction of generated material.
AI research and documentation support
Small but growing. Values people who can read a paper, summarise honestly, and flag what a claim does not show.
Source reading, structured writing, scepticism about benchmarks.
The consistent pattern: postings advertising “AI-first” roles still hire on conventional judgement — editorial, visual or analytical. Tool fluency is assumed, not differentiating.
Visual portfolio
Creative work produced alongside the technical study, used as the test bed for every tool evaluation.
Typographic systems
Grid and hierarchy studies, set in editorial formats and stress-tested at small sizes.
Generated image sequences
Directed series where the brief, not the model, determines the outcome.
Composite retouching
Combining generated and photographic material to a print-usable standard.
Layout studies
Long-form document design, tables, and data presentation.
Colour research
Restricted palettes tested for contrast and legibility.
Process documentation
Each piece shipped with the method, the failures and the decision points.
Process log
- 01
Establish the baseline
Audited existing skills against current job descriptions rather than course syllabi. Identified the gap as evidence, not ability.
- 02
Formal grounding
Enrolled on the MSc to place practical tool use inside a defensible theoretical frame, and to have assessed work as public proof.
- 03
Build in public
Every study week produces one artefact — a written analysis, an image set, or a documented workflow — logged with date and method.
- 04
Test against the market
Read AI-only remote postings weekly. Record required competencies, discard marketing language, and adjust the next week's work.
- 05
Design for capacity
Sessions are short and resumable. Nothing depends on being available at a fixed hour. Progress is measured in artefacts, not hours.
Tools used
- Language models
- Drafting, analysis, code reading, structured summarisation
- Image generation
- Concept development, iteration, art direction studies
- Design tools
- Layout, typography, retouching, portfolio production
- Python & notebooks
- Coursework, small experiments, data inspection
- Version control
- Tracking artefacts and process history
- Written documentation
- Process log, evaluation notes, role research
Next 30 days
- 01Publish three written evaluations of AI tools against a fixed production task.
- 02Complete the current MSc module artefact and add it to the visual portfolio.
- 03Extend the AI-only role dataset to fifty postings and summarise competency patterns.
- 04Produce one long-form case study connecting the creative work to a specific role type.
- 05Apply to a shortlist of genuinely remote, asynchronous positions.
Contact and enquiries
For remote or asynchronous roles, collaborations, or access to specific artefacts and certificates.
Written-first contact is preferred. A short brief with the role or question is enough to get a considered reply.
Working conditions: remote, asynchronous, no fixed-hour availability. Output is delivered as documented artefacts.