Daniel Binns is a media theorist and filmmaker tinkering with the weird edges of technology, storytelling, and screen culture. He is the author of Material Media-Making in the Digital Age and currently writes about posthuman poetics, glitchy machines, and speculative media worlds.
K1no looks… friendly. Image generated by Leonardo.Ai, 14 October 2025; prompt by me.
Notes from a GenAI Filmmaking Sprint
AI video swarms the internet. It’s been around for nearly as long as AI-generated images, however its recent leaps and bounds in terms of realism, efficiency, and continuity have made it a desirable medium for content farmers, slop-slingers, and experimentalists. That said, there are those who are deploying the newer tools to hint at new forms of media, narrative, and experience.
I was recently approached by the Disrupt AI Film Festival, which will run in Melbourne in November. As well as micro and short works (up to 3 mins and 3-15 mins respectively), they also have a student category in need of submissions. So over the last few weeks I organised a GenAI filmmaking Sprint at RMIT University last Friday. Leonardo.Ai was generous enough to donate a bunch of credits for us to play with, and also beamed in to give us a masterclass in how to prompt to generate AI video for storytelling — rather than just social media slurry.
Movie magic? Participants during the GenAI Filmmaking Sprint at RMIT University, 10 October 2025.
I also shared some thoughts from myresearch in terms of what kinds of stories or experiences work well for AI video, and also some practical insights on how to develop and ‘write’ AI films. The core of the workshop as a whole was to propose a structured approach: move from story ideas/fragments to logline, then to beat sheet, then shot list. The shot list, then, can be adapted slightly into the parlance of whatever tool you’re using to generate your images — you then end up with start frames for the AI video generator to use.
This structure from traditional filmmaking functions as a constraint. But with tools that can, in theory, make anything, constraints are needed more than ever. The results were glimpses of shots that embraced both the impossible, fantastical nature of AI video, while anchoring it with characters, direction, or a particular aesthetic.
In the workshop, I remembered moments in my studio Augmenting Creativity where students were tasked with using AI tools: particularly in the silences. Working with AI — even when it is dynamic, interesting, generative, fruitful, fun — is a solitary endeavour. AI filmmaking, too, in a sense, is a stark contrast to the hectic, chaotic, challenging, but highly dynamic and collaborative nature of real-life production. This was a reminder, and a timely one, that in teaching AI (as with any technology or tool), we must remember three turns that students must make: turn to the tool, turn to each other, turn to the class. These turns — and the attendant reflection, synthesis, and translation required with each — is where the learning and the magic happens.
This structured approach helpfully supported and reiterated some of my thoughts on the nature of AI collaboration itself. I’ve suggested previously that collaborating with AI means embracing various dynamics — agency, hallucination, recursion, fracture, ambience. This workshop moved away — notably, for me andmypredilections — from glitch, from fracture or breakage and recursion. Instead, the workflow suggested a more stable, more structured, more intentional approach, with much more agency on the part of the human in the process. The ambience, too, was notable, in how much time is required for the labour of both human and machine: the former in planning, prompting, managing shots and downloaded generations; the latter in processing the prompts, generating the outputs.
Video generated for my AI micro-film The Technician (2024).
What remains with me after this experience is a glimpse into creative genAI workflows that are more pragmatic, and integrated with other media and processes. Rather than, at best, unstructured open-ended ideation or, at worst, endless streams of slop, the tools produce what we require, and we use them to that end, and nothing beyond that. This might not be the radical revelation I’d hoped for, but it’s perhaps a more honest account of where AI filmmaking currently sits — somewhere between tool and medium, between constraint and possibility.
Here’s a recorded version of a workshop I first delivered at the Artificial Visionaries symposium at the University of Queensland in November 2024. I’ve used chunks/versions of it since in my teaching and parts of my research and practice.
For better or worse, I’m getting a bit of a reputation as ‘the AI guy’ in my immediate institutional sub-area. Depending on how charitable you’re feeling, this could be seen as very generous or wildly unfounded. I am not in any way a computer scientist or expert on emergent consciousness, synthetic cognition, language models, media generators, or even prompt engineering. I remain the same old film and media teacher and researcher I’ve always been. But I have always used fairly advanced technology as part of anything creative. My earliest memories are of typing up, decorating, and printing off books or banners or posters from my Dad’s old IBM computer. From there it was using PC laptops and desktops, and programs like Publisher or WordPerfect, 3D Movie Maker and Fine Artist, and then more media-specific tools at uni, like Final Cut and Pro Tools.
Working constantly with computers, software, and apps, automatically turns you into something of a problem-solver—the hilarious ‘joke’ of media education is that the teachers have to be only slightly quicker than their students at Googling a solution. As well as problem-solving, I am predisposed to ‘daisy-chaining’. My introduction to the term was as a means of connecting multiple devices together—on Mac systems circa 2007-2017 this was fairly standard practice thanks to the inter-connectivity of Firewire cables and ports (though I’m informed that this is still common even through USB). Reflecting back on years of software and tool usage, though, I can see how I was daisy-chaining constantly. Ripping from CD or DVD, or capturing from tape, then converting to a useable format in one program, then importing the export to another program, editing or adjusting, exporting once again, then burning or converting et cetera et cetera. Even not that long ago, there weren’t exactly ‘one-stop’ solutions to media, in the same way that you might see an app like CapCut or Instagram in that way now.
There’s also a kind of ethos to daisy-chaining. In shifting from one app, program, platform, or system, to another, you’re learning different ways of doing things, adapting your workflows each time, even if only subtly. Each interface presents you with new or different options, so you can apply a unique combination of visual, aural, and affective layers to your work. There’s also an ethos of independence: you are not locked in to one app’s way of doing things. You are adaptable, changeable, and you cherry-pick the best of what a variety of tools has to offer in order to make your work the best it can be. This is the platform economics argument, or the political platform economics argument, or some variant on all of this. Like everyone, I’ve spent many hours whinging about the time it took to make stuff or to get stuff done, wishing there was the ‘perfect app’ that would just do it all. But over time I’ve come to love my bundle of tools—the set I download/install first whenever I get a new machine (or have to wipe an old one); my (vomits) ‘stack’.
* * * * *
The above philosophy is what I’ve found myself doing with AI tools. I suppose out of all of them, I use Claude the most. I’ve found it the most straightforward in terms of setting up custom workspaces (what Claude calls ‘Projects’ and what ChatGPT calls ‘Custom GPTs’), and just generally really like the character and flavour of responses I get back. I like that it’s a little wordy, a little more academic, a little more florid, because that’s how I write and speak; though I suppose the outputs are not just encoded into the model, but also a mirror of how I’ve engaged with it. Right now in Claude I have a handful of projects set up:
Executive Assistant: Helps me manage my time, prioritise tasks, and keep me on track with work and creative projects. I’ve given it summaries of all my projects and commitments, so it can offer informed suggestions where necessary.
Research Assistant: I’ve given this most of my research outputs, as well as a curated selection of research notes, ideas, reference summaries, sometimes whole source texts. This project is where I’ll brainstorm research or teaching ideas, fleshing out concepts, building courses, etc
Creative Partner: This remains semi-experimental, because I actually don’t find AI that useful in this particular instance. However, this project has been trained on a couple of my produced media works, as well as a handful of creative ideas. I find the responses far too long to be useful, and often very tangential to what I’m actually trying to get out of it—but this is as much a project context and prompting problem as it is anything else.
2 x Course Assistants: Two projects have been trained with all the materials related to the courses I’m running in the upcoming semester. These projects are used to brainstorm course structures, lesson plans, and even lecture outlines.
Systems Assistant: This is a little different to the Executive/Research Assistants, in that it is specifically set up around ‘systems’, so the various tools, methods, workflows that I use for any given task. It’s also a kind of ‘life admin’ helper in the sense of managing information, documents, knowledge, and so on. Now that I think of it, ‘Daisy’ would probably be a great name for this project—but then again…
I will often bounce ideas, prompts, notes between all of these different projects. How much this process corrupts the ‘purity’ of each individual project is not particularly clear to me, though I figure if it’s done in an individual chat instance it’s probably not that much of an issue. If I want to make something part of a given project’s ongoing working ‘knowledge’, I’ll put a summary somewhere in its context documents.
But Claude is just one of the AI tools I use. I also have a bunch of language models on a hard drive that is always connected to my computer; I use these through the app GPT4All. These have similar functionality to Claude, ChatGPT, or any other proprietary/corporate LLM chatbot. Apart from the upper limit on their context windows, they have no usage limits; they run offline, privately, and at no cost. Their efficacy, though, is mixed. Llama and its variants are usually pretty reliable—though this is a Meta-built model, so there’s an accompanying ‘ick’ whenever I use it. Falcon, Hermes, and OpenOrca are independently developed, though these have taken quite some getting used to—I’ve also found that cloning them and training them on specific documents and with unique context prompts is the best way to use them.
With all of these tools, I frequently jump between them, testing the same prompt across multiple models, or asking one model to generate prompts for another. This is a system of usage that may seem confusing at first glance, but is actually quite fluid. The outputs I get are interesting, diverse, and useful, rather than all being of the same ‘flavour’. Getting three different summaries of the same article, for example, lets me see what different models privilege in their ‘reading’—and then I’ll know which tool to use to target that aspect next time. Using AI in this way is still time-intensive, but I’ve found it much less laborious than repeatedly hammering at a prompt in a single tool trying to get the right thing. It’s also much more enjoyable, and feels more ‘human’, in the sense that you’re bouncing around between different helpers, all of whom have different strengths. The fail-rate is thus significantly lowered.
Returning to ethos, using AI in this way feels more authentic. You learn more quickly how each tool functions, and what they’re best at. Jumping to different tools feels less like a context switch—as it might between software—and more like asking a different co-worker to weigh in. As someone who processes things through dialogue—be that with myself, with a journal, or with a friend or family member—this is a surprisingly natural way of working, of learning, and of creating. I may not be ‘the AI guy’ from a technical or qualifications standpoint, but I feel like I’m starting to earn the moniker at least from a practical, runs on the board perspective.
This semester I’m running a Media studio called ‘Augmenting Creativity’. The basic goal is to develop best practices for working with generative AI tools not just in creative workflows, but as part of university assignments, academic research, and in everyday routines. My motivation or philosophy for this studio is that so much attention is being focused on the outputs of tools like Midjourney and Leonardo.Ai (as well as outputs from textbots like ChatGPT); what I guess I’m interested in is exploring more precisely where in workflows, jobs, and daily life that these tools might actually be helpful.
In class last week we held a Leonardo.Ai hackathon, inspired by one of the workshops that was run at the Re/Framing AI event I convened a month or so ago. Leonardo.Ai generously donated some credits for students to play around with the platform. Students were given a brief around what they should try to generate:
an AI Self-Portrait (using text only; no image guidance!)
three images to envision the studio as a whole (one conceptual, a poster, and a social media tile)
three square icons to represent one task in their daily workflow (home, work, or study-related)
For the Hackathon proper, students were only able to adjust the text prompt and the Preset Style; all other controls had to remain unchanged, including the Model (Phoenix), Generation Mode (Fast), Prompt Enhance (off), and all others.
Students were curious and excited, but also faced some challenges straight away with the underlying mechanics of image generators; they had to play around with word choice in prompts to get close to desired results. The biases and constraints of the Phoenix model quickly became apparent as the students tested its limitations. For some students this was more cosmetic, such as requesting that Leonardo.Ai generate a face with no jewelry or facial hair. This produced mixed results, in that sometimes explicitly negative prompts seemed to encourage the model to produce what wasn’t wanted. Other students encountered difficulties around race or gender presentation: the model struggles a lot with nuances in race, e.g. mixed-race or specific racial subsets, and also often depicts sexualised presentations of female-presenting people (male-presenting too, but much less frequently).
This session last week proved a solid test of Leonardo.Ai’s utility and capacity in generating assets and content (we sent some general feedback to Leonardo.Ai on platform useability and potential for improvement), but also was useful for figuring out how and where the students might use the tool in their forthcoming creative projects.
This week we’ve spent a little time on the status of AI imagery as art, some of the ethical considerations around generative AI, and where some of the supposed impacts of these tools may most keenly be felt. In class this morning, the students were challenged to deliver lightning talks on recent AI news, developing their presentation and media analysis skills. From here, we move a little more deeply into where creativity lies in the AI process, and how human/machine collaboration might produce innovative content. The best bit, as always, will be seeing where the students go with these ideas and concepts.
I’m often asked by students for samples of writing that align with what’s required for assessment tasks. This semester is no different, so I actually spent some time digging through old courses and studios I’ve run, finding a few good examples that I can share with the students.
Very often my feedback on student reflections tends towards hoping they’ll integrate or synthesise research, ideas, and thoughts on their making. I usually find myself saying ‘take a position and argue it’, by which I mean that reflective writing — at least in an academic context — shouldn’t be about a summary of everything achieved, every decision made. Rather, choose a single point — be it a creative choice, or a quote from a journal article, or something watched — and then unpack that single point to make connections to other researchers and scholars, other makers, other reflections/insights the student generated in the class.
This is difficult to achieve, even for seasoned researchers. Add to this that the accepted conventions of academic writing — the vast majority of it in many fields — are so restrictive in terms of expression as to be incomprehensible. This means that students become terrified of approaching any academic writing. It’s seen as boring, or dense, or difficult. This greatly stifles their curiosity, or their interest in finding the connections I try to encourage.
If only, I hear them say or imply, academic writing was easier to engage with. Which reminds me that there are some truly wonderful, writerly, scholars out there. You just have to look. This is far from an exhaustive bibliography, but here are a handful of scholars that I read for the joy of experiencing good writing as much as for research.
Ingold, Tim. 2011. “The Textility of Making.” In Being Alive, 219–28. Milton Park: Routledge. https://doi.org/10.4324/9780203818336-28.
Jagoda, Patrick. 2016. Network Aesthetics. Chicago: University of Chicago Press. http://ebookcentral.proquest.com/lib/rmit/detail.action?docID=4427890.
Miles, Adrian, Bruno Lessard, Hannah Brasier, and Franziska Weidle. 2018. “From Critical Distance to Critical Intimacy: Interactive Documentary and Relational Media.” In Critical Distance in Documentary Media, edited by Gerda Cammaer, Blake Fitzpatrick, and Bruno Lessard, 301–19. Cham: Springer International Publishing.
Murray, Janet Horowitz. 2017. Hamlet on the Holodeck: The Future of Narrative in Cyberspace. Updated edition. Cambridge, Massachusetts: The MIT Press.
Peters, John Durham. 2015. The Marvelous Clouds: Toward a Philosophy of Elemental Media. University of Chicago Press. https://doi.org/10.7208/chicago/9780226253978.001.0001.
Pomerance, Murray. 2008. The Horse Who Drank the Sky: Film Experience beyond Narrative and Theory. New Brunswick: Rutgers University Press.
Stewart, Kathleen. 2011. “Atmospheric Attunements.” Environment and Planning D: Society and Space 29 (3): 445–53. https://doi.org/10.1068/d9109.
