"a dream we dream alone is just a dream, a dream we dream together is REALity... " Ono Yoko
A lecture & session course in creative practice
taught sessions with an applied project strand
working with materials, places, people, tools, and artificial minds
materials
places
people
tools
artificial minds
AI makes competent production increasingly easy.
The scarce human capacity is origination: noticing what matters, generating a live impulse, and retaining enough judgment to prevent powerful tools from making it ordinary.
Creativity is not treated as a rare talent or a sequence of software skills. It is a practice developed through contact with materials, tools, places, other people, constraints, accidents, and systems that answer back.
Students begin with hands-on, largely analogue practice. They learn to notice, improvise, acquire or make tools, work frugally, use available materials, and contribute to a shared creative environment. Only after establishing something genuinely their own do they introduce digital media and generative AI.
AI systems enter as fellow artists: different collaborators with distinctive capacities, temperaments, maintenance demands, and costs. Students learn to cast, test, judge, alter, or dismiss them according to what they contribute to the work and to the creative activity of the group.
Guiding principle: All tools are valid; all tools have costs.
The course is taught in an ordinary classroom or lecture theatre. Each session opens with a lecture and discussion; the practical work that follows uses only portable materials and the existing room. The wider campus is occasionally used as a site for observation or fieldwork, but the course requires no dedicated studio, specialist facility, or permanent change to any space.
The organising idea is straightforward: a class works best when its methods, materials, and shared records build on one another week to week, so that later sessions can draw on what earlier ones produced. A modest, well-chosen exercise usually teaches more than an elaborate technical set-up.
The course is a standard 15-week lecture course for 30 students, delivered in a classroom or lecture theatre. Every session is anchored by a taught lecture; students then apply the ideas in structured practical work, mostly in six continuing groups of five. The room needs only portable materials and temporary displays—no permanent change to the space.
Where two periods are scheduled back-to-back, each pair runs as one extended session—lecture and discussion first, applied work second:
Lecture and discussion: provocation; artist, movement, or historical context; demonstration; and framing of the applied task.
Individual or group making; midpoint constraint; group comparison; selected whole-class critique; documentation and reflection.
Tune: Place & Setting
Make: Hands (Body, mind, soul etc. ) & Materials
Resonate: Others & Systems. Feedback, resonance, emergence
Amplify: Digital & AI fellows
Encounter: Punlic & Community
Act on a creative impulse before its outcome is known.
Connect creative practice with relevant artists, movements, histories, and ideas.
Learn, adapt, misuse, or make tools in response to an artistic need.
Work intelligently with material limits, cost, resistance, accident, and failure.
Notice how place, atmosphere, and other people affect creative activity.
Develop work independently and as part of a collaborative group.
Translate an idea among physical, spatial, performative, digital, and generative forms.
Select AI collaborators according to their distinctive contribution and costs.
Distinguish technological fluency from artistic value.
Build works and systems that generate resonance and further activity.
Exercise authorship through selection, rejection, editing, and transformation.
Present work through increasingly public encounters, from one viewer to community.
[ link to table … ]
A: Creativity as practice rather than talent; divergent and convergent thinking; play, inhibition, and why AI makes origination more important. B: Rapid low-stakes making; establish process books and six working groups.
A: Site-specific art, walking as method, and relationships among space, season, material, and attention. B: Campus observation and field collection; groups construct a shared atlas of overlooked signals.
A: Studios, salons, laboratories, scenes, and temporary autonomous zones; examples from Frank’s practice. B: Groups propose and test a portable intervention that “tunes” the classroom.
A: DIY, maker and vernacular traditions; learning skill through purpose. B: A constrained construction challenge: learn, adapt, borrow, misuse, or make the tool the group needs.
A: Found material, readymades, Arte Povera, upcycling, and financial, environmental, attention, and maintenance costs. B: Create maximum expressive effect from minimal material expenditure.
A: Chance, indeterminacy, misuse, glitch, Dada, Fluxus, Cage, and the difference between randomness and judgment. B: Build a process that admits error; document what is accepted, repaired, or rejected.
A: Object, framing, context, audience, Duchamp, and institutional theories of art. B: Present the same object in three contexts; make a work for one other person.
A: Collaboration, collective practice, participation, assistants, audiences, and instruction-based work. B: One group writes instructions that another must interpret without clarification.
A: Feedback, emergence, cybernetics, generative systems, and artworks that respond rather than display. B: Build a classroom-compatible physical, social, or rule-based system that contributes to group activity.
A: Digitisation as translation; loss, compression, scale, replication, circulation, and the medium’s effect on the work. B: Translate an analogue fragment digitally and identify what was gained, lost, or normalised.
A: Generative models as differently behaving collaborators; authorship, selection, interfaces, and costs. B: Six groups test contrasting AI collaborators against the same living fragment.
A: Augmentation, automation, homogenisation, and the human as originator, editor, director, and judge. B: Produce human/material, AI-led, and authored-synthesis versions.
A: Infrastructure, dependence, preservation, obsolescence, and the question “Where exactly is the work?” B: Remove power or a key component and redesign the powered/unpowered relationship.
A: Gallery, salon, pop-up, performance, sequencing, framing, labels, hospitality, and documentation. B: Six groups design and test sections of a classroom or campus exhibition.
A: Install, rehearse, and refine through structured critique. B: Exhibition or salon; group presentation and individual reflection on impulse, tools, collaborators, costs, and authorship.
The tool test: Students identify an artistic need, then learn, adapt, borrow, misuse, or build the tool required to answer it.
Casting the fellow artist: Students give the same living fragment to contrasting AI systems and compare artistic contribution, maintenance demand, financial/material cost, and normalising pressure.
The power-off test: A digital or AI-mediated work is experienced with its technological infrastructure removed. Students decide where the work resides and redesign the shift between powered and unpowered states.
Expanding encounters: A work moves through increasing levels of publicness: one viewer → in-class showing → pop-up exhibition → salon/community encounter.
Assessment principle: Artistic value is not reduced to polish or technical complexity. Assessment follows attention, risk, specificity, resourcefulness, contribution to the shared system, and the relationship between the original impulse and its public form.
Component
Weight
What is assessed
Individual creative process book
25%
Selected weekly experiments, contextual understanding, decisions, failures, and concise reflection.
Two structured creative experiments
25%
Material/tool experiment and analogue–digital or power-off experiment. Demonstrated engagement with at least one generative-AI collaborator is required.
Group project and public exhibition
30%
Coherence, collective contribution, exhibition design, public encounter, and documentation.
Individual authorship reflection
20%
Clear account of impulse, tools, collaborators, AI, costs, transformations, and learning—including where AI was used and where it was deliberately refused.
The course operates as a recognisable lecture course: each week delivers taught content—artists, movements, histories, and ideas—and then connects it directly to a structured practical task. It stays responsive to the students and the materials at hand, but the teaching structure is conventional.
A taught lecture and visual case studies open every week, followed by applied work.
Thirty students organised into six continuing groups of five for the practical strand.
Portable material and tool kits; all practical work is classroom-compatible.
A shared class log recording tools, costs, failures, discoveries, and changes to working practice.
Individual process records alongside collective documentation.
Clear safety, equipment, access, and responsible-AI protocols.
Instigator: Keeps the original impulse and question visible.
Maker: Tests physical or digital execution.
Toolfinder: Identifies, learns, adapts, or makes what is required.
Editor: Removes generic or unnecessary elements.
Witness: Documents process, costs, failures, and decisions.
a short shelf, not a second syllabus — read to be provoked, not to be exhaustive
Suggested (enjoy these first)
Austin Kleon — Steal Like an Artist (2012). Short and energising; squarely on the course’s questions of influence, remix, and authorship.
David Gauntlett — Making is Connecting (2011/2018). Why making — by hand and with digital tools — builds meaning, learning, and community.
Grayson Perry — Playing to the Gallery (2014). A witty, accessible route into “when does it become art?”
Claude Lévi-Strauss — The Savage Mind (1962): the origin of “bricolage.”
Rebecca Solnit — Wanderlust: A History of Walking (2000).
Lucy R. Lippard — The Lure of the Local (1997) — or Miwon Kwon, One Place After Another (2002).
Hakim Bey — T.A.Z.: The Temporary Autonomous Zone (1991).
John Cage — Silence: Lectures and Writings (1961).
Germano Celant — Arte Povera (writings / exhibition catalogue, 1969).
Matthew B. Crawford — Shop Class as Soulcraft (2009): skill learned through purpose.
Jane Bennett — Vibrant Matter (2010): the agency of materials.
Roland Barthes — “The Death of the Author” (1967).
Sol LeWitt & Yoko Ono — “Paragraphs on Conceptual Art” (1967) and Grapefruit (1964): the instruction as artwork.
Claire Bishop — Artificial Hells: Participatory Art and the Politics of Spectatorship (2012).
Jack Burnham & Norbert Wiener — “Systems Esthetics” (Artforum, 1968) and The Human Use of Human Beings (1950).
Walter Benjamin — “The Work of Art in the Age of Mechanical Reproduction” (1935).
Kate Crawford — Atlas of AI (2021): the material and human costs behind the tools.
Joanna Zylinska — AI Art: Machine Visions and Warped Dreams (2020, open access).
Hito Steyerl — “In Defense of the Poor Image” (e-flux, 2009).
Brian O’Doherty — Inside the White Cube (1976).
Hans Ulrich Obrist — Ways of Curating (2014).
Grant H. Kester — Conversation Pieces: Community and Communication in Modern Art (2004).
look, then make; switch it on, then switch it off
Ways of Seeing — John Berger (BBC, 1972): the founding lesson in noticing.
The Way Things Go (Der Lauf der Dinge) — Fischli & Weiss (1987): chance, causality, and error as method.
Waste Land — Lucy Walker / Vik Muniz (2010): value made from found and discarded material.
Cybernetic Serendipity (ICA, 1968) — archival material, with Gordon Pask’s Colloquy of Mobiles: systems that answer back.
Sougwen Chung — drawing with robotic collaborators (artist talks / TED): AI as a fellow artist, live.
Anna Ridler — Myriad (Tulips) and Mosaic Virus (artist talks): making the dataset — and its cost — visible.
Memo Akten — Learning to See (2017, online): what a generative model projects onto the world.
Abstract: The Art of Design — Netflix (2017– ): accessible single-episode portraits of makers at work.
Links are deliberately omitted; all are findable by title through the library, the artists’ own sites, or the Institute’s subscriptions.
The course runs on everyday equipment that the classroom and the students already have. Nothing specialist, expensive, or purpose-built is required.
In the classroom
A projector or large screen for lectures, case studies, and group comparison.
A whiteboard and markers for teaching, planning, and the shared class log.
Tables and chairs that can be rearranged for discussion, group work, and showing.
Smartphones — the main tool for capturing images and sound, quick notes, and accessing web-based AI.
Laptops — for writing, editing, and running the AI and software tools in Phase IV (a shared or loaned device is provided for any student without one).
Paper — plain sheets and a few large flip-chart sheets for sketching, notes, and displays.
Pens, pencils, and markers.
Tape, scissors, and glue for simple assembly and display.
All accessed free or at low cost through a phone or laptop browser. The teaching point is contrast, so students compare more than one:
Text / multimodal models: e.g. Claude, ChatGPT, Gemini — compared directly in “Casting the fellow artist.”
Image and sound generators: a couple of free or trial web tools, so students feel the difference in cost, control, and “house style.”
Editing: free, browser-based or built-in apps on the phone or laptop — no paid software required.
Make costs explicit: keep a shared note of each tool’s price, data/consent terms, and footprint. This is course content, not admin.
Phone photos and notes, gathered into a shared cloud folder (institutional Drive/Teams).
The class log kept on the whiteboard and in the shared folder; individual process books kept by each student.
Access: every core activity is achievable without any personal purchase; a device is provided for anyone who needs one, so no student is disadvantaged.
Responsible AI: consent and attribution for any personal or third-party material given to a model; a stated position on training data, appropriation, and authorship (developed with students in Weeks 8 and 11); AI use logged in the process book and authorship reflection.