Creative AI

Welcome¶

You are reading the open textbook for Creative AI, a bachelor-level course at the University of Oslo (UiO). The course is open to students from all faculties, regardless of prior experience with programming, machine learning, or any particular art form. The book is also written for self-study and as a public resource for anyone curious about creative uses of AI.

The aim of the course is twofold:

1. To give you a working understanding of how today’s generative AI systems are built and why they behave the way they do.
2. To give you hands-on experience using these systems for creative work, alongside the critical vocabulary needed to discuss their cultural, ethical, and environmental consequences.

Three concepts thread through every week: intentionality, aesthetic control, and ethical authorship. The interesting question is not can the model do this, but what do you want, how do you guide the system there, and on whose terms.

Course at a glance¶

Course description (for the course catalogue)¶

Artificial intelligence has moved from a back-office technology into a tool that shapes everyday creative work: writing, drawing, photography, music, video, design, code, journalism, scholarship, and teaching. Creative AI introduces students from across the University of Oslo to the concepts, tools, and ethics of working creatively with contemporary AI systems.

The course assumes no prior programming or specialised art background. Through a combination of short lectures and weekly hands-on labs, students learn how today’s text, image, sound, video, and multimodal models work; how to direct and refine their outputs; how to integrate them responsibly into their own discipline; and how to reflect critically on the cultural, legal, environmental, and political stakes of the technology.

Students leave the course with a small portfolio of creative AI artefacts, a documented prompt and decisions log, and a final project presented at a public end-of-semester Synthetic Gallery showcase at UiO.

Learning outcomes¶

Following the standard UiO three-tier format:

Recommended background¶

• No programming experience required.
• Basic digital literacy (web browsing, document editors, file management) is assumed.
• Curiosity from any discipline is more important than any specific prior knowledge.

Students who already program will find an optional “code track” in each weekly chapter (clearly marked) — typically a short Python notebook that opens the hood on the same idea explored in the no-code lab.

Modes of teaching and learning¶

Each weekly cycle follows the same shape:

1. Before class — read the week’s chapter (≈ 1 hour). The textbook is meant to be read before the lecture, not in place of it.
2. Lecture (1 h) — short talk, demos, class discussion. Many lectures feature a guest from another UiO faculty or research centre (RITMO, IFI, IMV, Department of Media and Communication, KHiO, etc.).
3. Lab (2 h) — guided hands-on session, structured around three intertwined tracks: Reflect, Explore, and Create (see the next section). You will work alone or in pairs with real AI tools, produce a small artefact (a paragraph, an image, a sound, a sketch, a short video, or some code), and discuss results with peers.
4. Self-study (≈ 6 h) — reading, follow-on experimentation, work on assignments and the final project.

In week 0 (the week before teaching starts) all students complete a short AI-literacy onboarding module: account setup for the term’s tools, a privacy briefing, and the AI at UiO guidelines for student use of AI.

Course schedule¶

The 12-week schedule below maps lectures, practice labs, and assignment milestones. Practice sessions deliberately mix modes: some weeks you will use commercial tools in the browser; other weeks you will write a few lines of Python in a notebook on your laptop or in UiO Educloud.

Pedagogical strategy¶

A course for everyone at UiO¶

Creative AI is not a specialist course in computer science, art, or media. It is designed as a general education course: students arrive from law, medicine, musicology, design, theology, mathematics, dentistry, education, literature, biology, and many other places. That diversity is the point. The classroom is itself a small interdisciplinary laboratory where lawyers and artists, medics and historians can ask hard questions of the same AI tool and watch each other’s answers.

We assume no programming background, but each weekly lab provides an optional code track for students who want to look under the hood. Where we use code, it is in Python notebooks that you can run in your browser without installing anything.

Active learning and a flipped classroom¶

This course is built around active learning and a flipped classroom model: the chapter is read before the lecture; the lecture is the place to argue, demo, and answer the questions you bring in; the lab is where you make things.

Studio-based labs and process over polish¶

The labs are run studio-style: short briefs, fast iteration, peer feedback, instructor and TA on hand. The assessment philosophy follows from this — we grade process, reflection, and deliberate decisions over technical perfection. Risk-taking, honesty about failure, and originality are explicitly rewarded.

Guest lecturers from across UiO¶

Where possible, each week’s lecture features a guest from a UiO department or research centre whose work intersects with the week’s theme: e.g., RITMO for sound and music, IFI for the machine-learning weeks, IMK for the ethics and media weeks, KHiO and architecture for design weeks, the Law Faculty for the copyright discussions, NB AI Lab and the National Library for language and audio in Norwegian.

Research-based and research-led¶

This is a research-based course: the content rests on current research from machine learning, human–computer interaction, media studies, the humanities, and the arts. It is also research-led: the teachers are themselves working on Creative AI projects, and parts of the course (in particular the final-project showcase) feed into ongoing research at UiO and partner centres.

A note on AI tools used to write this book¶

This textbook is itself an example of AI-supported authorship. Drafts of every chapter have been written collaboratively with large language models, then revised, fact-checked, and re-organised by human editors. Where AI tools have produced figures or examples, we say so. We treat the book as a living document — please open an issue or a pull request on the GitHub repository when you spot errors or omissions.

Reflect, Explore, Create — three weekly tracks¶

Every week the lab is organised around three intertwined tracks. They are not three separate assignments — they are three modes of engagement that together make up a Creative-AI practice. Most weeks you will do at least one short activity in each track.

Reflect¶

Critically study and discuss the impacts of AI on humans, human creativity, cultures, and society at large. This is the humanities and social sciences track: ethics, history, aesthetics, politics, law, learning. Reflect activities ask why we are doing this, who pays for it, who benefits, and who is left out.

Typical outputs: short critical writing (150–300 words) in your weekly log; in-class debates; ethical audits; comparative readings of two tools or two cases; honest captions and provenance notes for your own work.

Explore¶

Use AI-based systems in creative practice — and see how creative methods can be applied in other domains. This is the applied and behavioural track: psychology, therapy, educational sciences, cultural heritage. Explore activities investigate how AI can enhance creativity, foster innovation, and support learning and well-being — by trying things and documenting what the tools actually afford.

Typical outputs: controlled experiments (same prompt, vary one knob); prompt comparisons across two tools; hallucination hunts; model-card analyses; tool-to-task mappings; failure-mode catalogues.

Create¶

Make AI-based systems, tools, artworks, frameworks, and policies. This is the making track: computer science, engineering, art, design — with an explicit emphasis on Co-Creative AI systems that prioritise human agency, environmental sustainability, and the democratisation of AI technologies. Each lab leaves you with a concrete artefact for your portfolio.

Typical outputs: images, songs, short videos, code sketches, design pipelines, prototype agents, policy briefs, exhibition pieces.

How the tracks connect¶

The three tracks reinforce one another. Reflect asks why you are doing something; Explore asks what the tool actually does; Create asks what you can make with that understanding that you stand behind. A good Creative-AI practitioner never separates them for long.

The Reflect / Explore / Create rhythm also maps onto the disciplinary breadth of this course (and of the fourMs Co-Creative AI research at RITMO that gave it its shape): humanities and social sciences on the Reflect side; applied and behavioural sciences on the Explore side; computer science, engineering, art, and design on the Create side. Students from every background contribute on every track.

Tools we will use¶

Across the semester you will meet many tools. The list below is not exhaustive, and any of it may change as the field moves. The point is to learn the categories, so that you can evaluate the next tool that appears.

Reading list¶

The textbook itself is meant to carry the conceptual load of the course. The readings below are complements and deepenings — short essays you can read in an evening, books you can dip into around the chapter that needs them, and a few classics that anyone working with AI in cultural production should meet at least once. Many of them are openly available; the rest are in the UiO library.

You are not expected to read all of them. Pick one core title to read alongside the textbook over the semester, and dip into the supplementary list around the weekly topics that pull you.

Core curriculum¶

A short, deliberately broad list. Pick one to read in parallel with the textbook:

• Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux Mitchell, 2019. — the most readable contemporary introduction to AI for a general audience, by an AI researcher who refuses to oversell. Best technical anchor.
• Hertzmann, A. (2018). “Can Computers Create Art?” Arts 7(2):18 Hertzmann, 2018. — short, open-access essay covering the philosophical question that hovers over the whole course. Best creative-AI anchor.
• Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press Crawford, 2021. — structural critique of where the data, the labour, and the energy come from. Best critical anchor.
• Bridle, J. (2022). Ways of Being. Allen Lane Bridle, 2022. — contemporary, accessible reframe of “intelligence” beyond the human-vs-machine binary. Best forward-looking anchor.

If you read only one of these, pick Mitchell for the technical side or Hertzmann for the creative side. If you read two, pair one of those with Crawford or Bridle.

Supplementary reading by theme¶

Each weekly chapter ends with a Going further section that adds chapter-specific suggestions on top of this list.

Open Education and Open Research¶

The textbook follows the principles of Open Education and Open Research: the material is openly licensed (CC-BY-4.0), the source is on GitHub, and we point to open tools and datasets where possible. Where a tool requires a paid account, we say so and try to offer an open alternative.

This is also a political stance. Creative AI is being built and deployed mainly by a handful of large companies. Treating the study of Creative AI as an open, collaborative project is one small way to push back.

Assessment¶

There is no traditional written exam. The expectation is that you can talk and write coherently about what you made, with what tools, and why. Assessment is portfolio-based and is structured as a ladder of increasingly ambitious deliverables:

Process memo¶

Each graded assignment is submitted with a short process memo answering, at minimum, two prompts that we will use all semester (adapted from the practice-based tradition at RITMO and from earlier creative-AI courses):

1. Surprise. Where did the AI surprise you — pleasantly or unpleasantly — and what did that teach you about the tool?
2. Will. Where did you exert your own creative will over the output — through prompt, edit, refusal, selection, or composition?

These two questions are the centre of gravity of the course. If you can answer them honestly across all your work, you will pass.

The final project — “The Synthetic Gallery”¶

The final project is presented at a public mini-exhibition in week 12 (the Synthetic Gallery), open to other UiO students and staff. The exhibition lives both physically (in a UiO common space) and online (as a static gallery on the course’s GitHub Pages site). Selected projects, with consent, are kept in the public gallery for future cohorts.

Final-project requirements:

• Any medium — text, image series, short film, song / EP, podcast episode, interactive sketch, small game, redesign of a real organisation’s brand, critical essay, teaching resource, etc.
• The work must use at least two different AI modalities (e.g., text + image, image + video, audio + code) — this is the technical bar.
• Solo or groups of 2–3.
• Documented with a full prompt-and-decisions log and a 1 500–2 500-word reflection.

How to read this book¶

The chapters are roughly linear, but they are also self-contained. If you are a complete beginner, read in order. If you already have some background, you can skim chapters 2–3 and jump into the application chapters that interest you (text, image, sound, video, code).

Every chapter follows the same shape:

1. Why this matters — a short framing.
2. Concepts — the ideas you should be able to explain after reading.
3. Examples — concrete cases, screenshots, code snippets.
4. This week’s lab: Reflect, Explore, Create — the three-track 2-hour lab session, with one or two activities in each of Reflect, Explore, and Create.
5. Going further — readings, tools, links.

The order inside the lab section is deliberate: we start by reflecting on what is at stake, then explore what the tools actually do, then create something we stand behind. Most students will spend 30–45 minutes on Reflect, 30–45 minutes on Explore, and 45–60 minutes on Create — the exact split varies week by week, and you are welcome to bend it to your own discipline.

Let’s begin.