The Clockwork Penguin

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.

Category: Teaching

  • Cinema Disrupted

    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 my research 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 and my predilections — 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.

  • Conjuring to a brief

    Generated by me with Leonardo.Ai.

    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.

  • What makes good academic writing?

    Photo by Pixabay from Pexels, 21 December 2016.

    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.
  • The Adrian Miles Reading List

    Screen Shot 2018-02-09 at 2.05.34 pm

    I and many others in the RMIT community are struggling to find ways to deal with the loss of our dear colleague and friend Adrian Miles. Adrian had a profound impact on me in a very short space of time. My current book project has a foundation in many of the challenging ideas he threw at me; so much so that picking up work on it again will be tough.

    Finding words is something Adrian never struggled with. I thought I’d collate some of the hundreds upon thousands he foisted on colleagues, students, and friends. Suggestions welcome in the comments: I’ll update the post with any additions.

    If you’re wondering how best to remember Adrian, maybe pick up one of the following, or take 25 minutes’ silence, with a 5-minute break.

     


     

    Bogost, Ian. (2012). Alien Phenomenology, or What it’s Like to be a Thing. University of Minnesota Press.

    Ingold, Tim. (2011). “Rethinking the Animate, Reanimating Thought.” Being Alive: Essays on Movement, Knowledge and Description. Routledge.

    Latour, Bruno. (1987). Science in Action: How to Follow Scientists and Engineers Through Society. Harvard University Press.

    Pickering, Andrew. (1995). The Mangle of Practice: Time, Agency, and Science. University Of Chicago Press.

    Stewart, Kathleen. “Atmospheric Attunements.” Environment and Planning D: Society and Space 29 (2011): 445–453.

    Vannini, Phillip. (2015). Non-Representational Methodologies: Re-Envisioning Research. Routledge.

  • Priorities

    I am lucky to have a job that I love. But in the eighteen months of settling into full-time academia, I seem to have lost sight of the ‘love’ and become fixated on the ‘job’. A weird thing has happened in recent weeks, in that I’ve tried to become more focused on what is actually important about my work — and what feels the most rewarding.

    There are two main strands to the workload of an academic at my level: teaching and research. Research covers the writing and publication of scholarly work — be it journal articles, book chapters, conference presentations, monographs. Teaching is what it says on the tin.

    In 2011, mid-PhD, I took my first class at Western Sydney University (then UWS). It was a boring compulsory course, but I caught the bug, and have loved teaching ever since. With the transition to full-time employment, I’ve always tried to have time for my students, time to sink into my pedagogy, but that time has always felt sapped by other commitments. I say felt, because I’ve realised that the sapping of time has only occurred because I’ve let it.

    This semester, I’ve turned a corner. The most important commitments I have, during semester time, are my students. Everything else is secondary. To be clear, I don’t think the time I spend on teaching or research will change this semester (I have a book chapter to finish, a presentation to write, and a monograph to approve all by September). Rather what has changed is where my head is at most of the time: ensuring my students are, if not blissfully happy, then at least reasonably clear about what I’m trying to teach them, and the experience I would — ideally — like them to have.