Now that you can make things, what do you have to say, and what do you owe your audience?
By the end of Day 4, AI literacy includes public explanation. Students select, title, contextualize, and share their strongest work, then write process notes that explain what they made, how they iterated, and what the model contributed. The final question is not just whether the piece works, but whether the student can describe the system, the choices, the limits, and the ethical label clearly.
Schedule
Block 1: instructor demo, class co-construction, student studio time
Block 2: second lesson sequence or guided production sprint
Break: 15 minutes away from the screen
Block 3: studio work with instructor/tutor nudges
Block 4: finish, export, document
Daily share-back: every student shows one decision
Reflection card and next-day preview
Lessons
Lesson 4.1 - Polish and package
Pick the strongest pieces and write process notes (artist statement) that belong with them.
I do / We do / You do
I do: 15 min: instructor walks through concise process notes.
We do: 10 min: class workshops one volunteer note.
You do: 25 min: each student selects 2-3 pieces and writes a 100-word statement for each.
Deliverable: 2-3 portfolio entries staged in ij8, or MP4 exports plus process-note documents in the shared folder.
Stretch: Title each work intentionally and record a 30-second audio statement.
AI tutor aiGuidance excerpt
- nudge: Ask what the viewer should notice first.
- nudge: If the process note only describes the tool, ask for intent and revision history.
- require: Label work as AI-assisted or AI-generated where appropriate.
Lesson 4.2 - Showcase and reflection
Show your work, see others' work, and describe what you saw.
I do / We do / You do
I do: 10 min: set up gallery walk and warm/cool feedback.
We do: 60 min: class gallery walk with stations, process notes, and feedback cards.
You do: 15 min: each student writes a 150-word reflection on the strongest peer work they saw.
Deliverable: Portfolio published in ij8 if the gallery is live, or MP4s collected in the shared folder, plus reflection submitted.
Stretch: Publicly call out one classmate piece in the closing circle.
AI tutor aiGuidance excerpt
- nudge: Ask for one warm observation and one cool question, not a rating.
- nudge: If share-gallery is unavailable, guide the student to export MP4 and attach the process note in the shared folder.
- require: Do not publish identifiable minors or uncited reference-based work.
How the tech works
Day 4 uses ij8 as a studio and packaging surface, not as a magical publishing guarantee. The current platform can export MP4s through the share-video pathway, and a student-facing share gallery is in active development; therefore every instruction names both paths. Students either publish to their portfolio in ij8 if the gallery is live, or export MP4 files and collect statements in the shared folder. The technical idea is curation: choose a small set, preserve process notes, and present the work with context. The AI tutor should nudge toward intent, attribution, and revision history rather than generating a glossy biography. Showcase protocols from PBLWorks and EL Education support critique as evidence, not applause. The technical workflow is intentionally conservative because an unfinished gallery feature should not determine whether students can complete the course. If the gallery ships, students publish inside ij8. If it does not, they export MP4s or collect files in the shared folder and attach process notes. In both cases the assessed outcome is the same: selected work, process context, peer feedback, and reflection. The platform supports the pedagogy; it is not the pedagogy itself (PBLWorks gallery walks; Tuning Protocol).
A short history
The "is AI art real art" question is too blunt for the end of the week. A better history asks how artists have used systems, tools, collaborators, and constraints. Harold Cohen worked with AARON across decades; Holly Herndon built PROTO with an AI vocal model named Spawn; Sasha Stiles works with language models as poetic collaborators; Jason Allen's 2022 Colorado State Fair win turned public attention toward disclosure and competition categories. Day 4 places students inside that ongoing authorship debate rather than outside it. The history also helps students understand why disclosure can be an artistic strength rather than an apology. Many important works are interesting precisely because they reveal the system, collaborator, dataset, or constraint behind them. Strong process notes can do the same: it can name the tool without making the tool the whole story.
Ethics for this day
The Day 4 ethics question is attribution. Students should distinguish AI-assisted from AI-generated, cite artists and works they reference, and say what role the model played. Copyright lawsuits such as Getty v. Stability AI and NYT v. OpenAI are ongoing, not settled, so the site avoids declaring winners (copyright litigation overview). Environmental cost also returns here: large AI systems consume energy and water, and students should treat generation as an intentional studio act, not infinite free material. The environmental point is not to shame experimentation; the workshop depends on experimentation. The point is to replace endless unexamined generation with deliberate iteration: make a batch, study it, revise with purpose, and stop when the work has enough evidence to support the idea (NEA environmental impact; NSTA environmental impact).
Real-world applications
Portfolio review for college applications
Artist-statement craft
Gallery and museum exhibition habits
Residency or festival applications
Peer critique and revision routines
Famous works to study
Refik Anadol, in situ: Refik Anadol (2025). Guggenheim Bilbao exhibition from the artist’s AI-data practice. Source.
Sasha Stiles, A Living Poem (2025-26). MoMA Hyundai Card Digital Wall language-and-AI poetry installation. Source.
Tyler Hobbs and Dandelion Wist, QQL (2022). Collaborative generative system and collecting project following Fidenza. Source.
Holly Herndon, PROTO (2019). Album and performance project built with the AI vocal model Spawn. Source.
What you should be able to do by the end of today
Select 2-3 works with a reason.
Write process notes that include intent, process, and attribution.
Give warm and cool feedback to peers.
Publish or export work through the available path without relying on an unfinished feature.
Deeper look
Optional note for the curious
Curation is an edit. A portfolio is stronger when it shows a point of view, not everything made during the week. Leaving a piece out can be an authorship decision.