I’ve been working with the metaphor of digital plastic for a while now: first to sketch the “geological layer” GenAI is leaving across the web in Digital plastic: Generative AI and the digital ecosystem, then to unpack production and meaning in The Semiotics of Synthetic Media: Production and Meaning in Digital Plastic, and more recently to ask whether the flood is miraculous—or graceless slop—in Digital Plastic: Miraculous substance or graceless slop?.
In a new peer-reviewed article with Jasper Roe and Mike Perkins, we develop the metaphor further and situate it in multiliteracies and Critical AI Literacy (CAIL): Digital plastic: a metaphorical framework for CAIL in the multiliteracies era. The short version: like physical plastics, GenAI outputs are cheap, malleable, ubiquitous, and persistent. That mix creates access and creativity and pollution and inequity.
Digital Plastic in practice
Here are a few ideas for using this concept in the classroom. Read the paper above for more.
1) Treat synthetic media as a “material” with properties.
When students (or staff) use GenAI, get them to name the properties they’re choosing: pliable, fast, homogeneous, biased, persistent. Ask critical questions about the purpose of synthetic media, like: What does this synthetic media do well? What might it harm? Tie that back to the ecosystem idea in the original post.
2) Design for deliberate multimodal use.
Don’t necessarily ban the “plastic” – but be prepared to specify the intent and the utility. If the goal of a lesson is media creation, consider the implications of text-to-video or image-to-video generation. Young people will have increasing access to these technologies, especially as companies like Meta partner with companies like Midjourney. The examples below are short videos generated in the latter platform. What does social media look like when every person on Instagram, Threads, Facebook or Whatsapp have free and on-demand access to this?
And this “digital plastic” is already starting to extend into more interactive multimodal texts like playable videogames. The below, from Mirage 2, was created using a still image (generated in Midjourney) and a text prompt. In the video you’ll see me controlling the character using my keyboard, running, jumping, and looking around. It’s very early days and very glitchy. Essentially the model is generating new images at around 30 frames per second. But what does this look like in two years’ time?
3) Make provenance part of the design brief.
Multimodal submissions that use GenAI include a short “supply chain” of data provenance: prompts, tools, models, filters, and what was kept/changed, but also an analysis of where the models’ training data came from, and other relevant processes. That habit both demystifies the process and foregrounds critical judgement: exactly what we want when the default “big tech” position is frictionless generation. I explore the semiotic side of this in the semiotics post.
Metaphors as a toolkit for CAIL
I wrote about why metaphors for Critical AI Literacy can be useful in a previous article, Using Metaphors to Teach Critical AI Literacy, which links to our open-access JIME publication, Reflecting Reality, Amplifying Bias? Using Metaphors to Teach Critical AI Literacy. If “digital plastic” helps with persistence, pollution and affordances, pair it with AI as map (representation and power), funhouse mirror (distortion), or black box (opacity). Use each metaphor to ask: who benefits, who’s absent, what counts as evidence, and when should we refuse the tool? The JIME piece also includes some ready-to-run activities.
The metaphor of “digital plastic” keeps two truths in view: GenAI could widen access to multimodal composition, and it could entirely flatten our digital commons. Framed this way, we don’t chase hype or panic; we design for purpose, insist on provenance, and teach the habits that keep human judgement central even when the material is synthetic.
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