COURSE SYLLABUS

COURSE INFORMATION

AIMS & OBJECTIVES

Generative AI Workflow for Media introduces an AI-driven workflow for media art rather than treating generative AI as a single making tool. The course positions AI agents—including, but not limited to, ChatGPT, Claude, Gemini, and Kimi—as collaborators that can support learning, research, ideation, making, documentation, critique, and reflection. Students will build individual ways of working with agents while retaining responsibility for artistic intention, source verification, technical decisions, authorship, and ethical judgement.

The course is organised around a five-stage collaborative learning and making cycle:

1. Learn from AI — use an agent as a conversational tutor to clarify unfamiliar concepts, compare perspectives, and identify questions for further study.
2. Research with AI — build source-aware workflows for searching, collecting, organising, checking, and citing information, references, and creative datasets.
3. Ideate with AI — turn research, observation, and personal interests into original artistic questions, concepts, and project proposals through dialogue and iteration.
4. Create with AI — translate an idea into a media artwork through agent-assisted coding, prototyping, asset production, troubleshooting, and refinement.
5. Reflect with AI — critically evaluate the finished work and its process, incorporate human feedback, disclose AI involvement, and communicate learning through a website, presentation, and short reflective essay.

By the end of the course, students should be able to:

• develop a personal, iterative, agent-assisted workflow for art practice and academic research;
• understand generative AI and AI art in relation to net art, creative coding, moving-image practice, and independent/art games;
• prototype across three media-art pathways: web-based creative coding, AI-assisted animation/film/video, and an AI-driven interactive game;
• collect and verify sources, distinguish evidence from model-generated claims, and document prompts, tools, datasets, iterations, failures, and decisions;
• create a self-authored course website that functions as a public-facing portfolio and a living archive of learning, development, feedback, and reflection;
• make and present an original final media artwork in one selected pathway;
• critically address authorship, bias, copyright, consent, privacy, labour, environmental impact, accessibility, and the cultural consequences of AI-mediated production.

COURSE CONTENTS

1. Generative AI, Agents, and Contemporary Media Art

An accessible introduction to artificial intelligence, machine learning, deep learning, foundation models, generative models, multimodal models, and agent-based systems. Students examine how AI has shifted from a content-generation tool to a participant in research, planning, production, coding, and evaluation. Core limitations—including hallucination, bias, model opacity, platform dependency, and rapidly changing capabilities—are addressed from the outset.

2. AI Art: Histories, Contexts, and Critical Questions

An introduction to AI art and its relationship to computational art, net art, creative coding, generative art, animation, film, games, and interactive media. Seminars consider artistic agency, authorship, originality, dataset politics, copyright, consent, representation, labour, sustainability, and the aesthetics of automation.

3. Agent-assisted Learning and Research

Students practise the Learn from AI and Research with AI stages through questioning, search planning, source comparison, annotation, citation tracing, fact-checking, reference management, and the construction of small research or inspiration collections. AI responses are treated as leads to investigate, not as authoritative sources.

4. The Course Website as a Living Studio and Portfolio

Each student will design and maintain an individual website with agent-assisted web development. The website is the central container for the entire course and must include the following first-level navigation:

• Home — project identity, current focus, and selected highlights;
• Visual Journey — weekly research and observation logs containing collected references, annotations, sources, and personal responses;
• Idea Presentation — a web-based project proposal equivalent to a concise slide presentation;
• Working Log — dated development notes, experiments, prompt/workflow records, technical tests, failures, revisions, and decisions;
• [Project Title] — the final project page, hosting the work directly where feasible or linking clearly to an external build/video;
• Feedback — documented peer, tutor, visitor, or user feedback, with the student's response and resulting revisions; and
• About — author biography, interests, role, contact/portfolio information as appropriate, and a transparent AI-use statement.

The website should demonstrate legible information architecture, responsive behaviour, accessibility awareness, appropriate attribution, versioned development, and a coherent visual identity. Students may use static-site tools or web frameworks appropriate to their experience, but must be able to explain and maintain what they publish.

5. Visual Journey, Collection, and Ideation

Students collect material every week: artworks, texts, interfaces, moving images, sounds, games, cultural observations, datasets, and questions. Entries must move beyond bookmarking by including source information, a brief interpretation, and a connection to the student's developing interests. Approximately one-third into the course, students synthesise this archive into an individual idea presentation defining an artistic question, context, audience, intended experience, selected medium, proposed AI role, risks, and production plan.

6. Pathway A — Net-based Creative Coding

Students explore net art and browser-based creative coding, primarily through p5.js. They use agents to explain code, generate and revise small functions, debug, test interactions, and consider alternative implementations. The practical outcome is a small web-based creative program with purposeful visual or sonic behaviour, basic interaction, and clear artistic intent. Students must test generated code and be able to explain its main logic.

7. Pathway B — AI-assisted Animation, Film, and Video

Students explore an AI-assisted moving-image workflow: research, concept development, script or score, visual development, storyboarding, image/video/audio generation, optional 3D assets, editing, sound, compositing, and critical revision. The emphasis is not tool spectacle but audiovisual coherence, temporal structure, ethical sourcing, and an intentional relationship between AI processes and the work's meaning.

8. Pathway C — AI-driven Game and Interactive Experience

Students develop a small art game or interactive experience, with Unreal Engine as the principal platform. Agents and, where appropriate, MCP-enabled workflows may support planning, Blueprints/code, asset preparation, troubleshooting, and iteration. The outcome should be a focused, playable interaction or scene whose mechanics, spatial design, feedback, and artistic concept are meaningfully connected. Scope control and build reliability are prioritised over feature quantity.

9. Project Planning, Development, Feedback, and Critique

Students select one of the three pathways for the final project. They define a feasible scope, milestones, toolchain, AI role, risk register, and evaluation method; then develop through studio workshops, individual tutorials, peer testing, formative critiques, and a field-based learning activity. Feedback is recorded on the website and translated into specific revisions.

10. Documentation, Presentation, and Reflective Writing

Students prepare the final website as the presentation interface for the course. It must connect the Visual Journey, idea presentation, Working Log, feedback, final artwork, and a short reflective essay into a coherent account of practice. The essay should discuss intention, context, workflow, major decisions, AI-human collaboration, evidence of iteration, ethical issues, outcomes, limitations, and future development.

COURSE LEARNING OUTCOMES (CILOs)

Programme Intended Learning Outcomes (PILOs)

The following PILOs are retained from the source syllabus for programme-level alignment.

CILOs–PILOs Mapping Matrix

TEACHING & LEARNING ACTIVITIES (TLAs)

A typical three-hour session combines approximately 50 minutes of seminar/context, 100 minutes of demonstration and workshop/studio activity, and 30 minutes of critique, reflection, or planning. Breaks are integrated into the session.

ASSESSMENT METHODS (AMs)

This course contains no final examination due to its practice- and project-based orientation.

Final Project Criteria

The final project is evaluated through the artwork and the essay. The criteria below guide the assessment across all three pathways.

• Artwork — thematic originality, artistic quality, and value: The work presents an original and engaging idea with clear artistic intention, insight, and relevance.
• Artwork — development process and implementation: The project shows a considered technical route, meaningful iteration, problem-solving, and a work that is genuinely realised.
• Artwork — final presentation and documentation: The work is presented effectively in its intended exhibition, screening, browser, installation, or play context, with a convincing display and video record.
• Essay — clarity of expression: The essay is clearly written, well organised, and able to explain the project's concept, process, decisions, and human–AI collaboration.

Pathway-specific interpretation

• Net-based creative coding: interaction quality; relationship between code/system behaviour and artistic intent; browser performance; responsive presentation; code legibility and the student's ability to explain agent-generated or agent-revised logic.
• AI-assisted animation/film/video: audiovisual language; temporal structure and editing; consistency or purposeful variation across generated assets; sound-image relationship; ethical provenance and transformation of source/generated material.
• AI-driven game/interactive experience: meaningful relationship between mechanics and concept; playability; interaction feedback; spatial and experiential design; build stability; coherent integration and optimisation of assets.

Submission Components

The course submission package includes:

• a functioning public or locally deployable course website;
• the final artwork or a stable access link, build, or video;
• a complete Visual Journey and Working Log;
• the idea presentation and evidence of feedback-led revision;
• a concise AI-use statement identifying key models/tools and the student's decisions;
• source, dataset, asset, and collaborator credits where applicable;
• a short reflective essay, with the final word count to be confirmed after programme-level assessment requirements are checked.

COURSE SCHEDULE

This is a working schedule. Dates, topics, and assessment checkpoints may change with the institutional calendar, field-trip arrangements, and project development.

READINGS

The course readings combine critical histories and theories of AI art with studies of net and post-internet art, creative coding, moving-image practice, games, and interactive media, supplemented by current technical documentation, artist writings, and case studies. Together they provide historical context, conceptual vocabulary, and practical reference for examining authorship, agency, machine affordance, automation, datasets, interfaces, participation, ethics, and the changing distribution of creative labour between people and intelligent systems. Students use the readings to connect precedents to their selected pathway, verify claims encountered through AI tools, and develop a critically situated account of their own human–AI workflow. The complete bibliography and access links are maintained on the website’s Reading page.

COURSE DELIVERY NOTES

Last revised: 3 August 2026