AF7620 Future Filmmaking and Technologies

How AF7620 is assessed

The three components published in the teaching plan total 100%. The pitch is marked on four published criteria: innovation and strategic thinking 40%, producerial acumen 30%, creative foundation, character design and world-building 20%, and professionalism and clarity 10%.

Three rules sit outside the weighted table. The teaching plan states that students must submit all projects and take part in every stage of the final project to pass; work handed in late receives no mark unless the lateness was approved in advance; and any AI-generated images, audio or text you submit must be accompanied by a list of the prompts and applications used.

The plan describes the co-op assignment only as work whose details come from industry partners; the course site places an AI short film for competition, of at least one minute in .mp4 with sound, under the first assignment in the weeks the plan gives to the co-op project. Confirm the current brief, deadlines and submission settings on Moodle.

What AF7620 covers

Ten chapters follow the published lecture sequence from AI for creatives and design styles through story, character and film language to the production pipeline, AI consistency, transmedia, copyright and the final pitch.

Science fiction epics, period dramas and creature-filled fantasy have long belonged to studios with the largest budgets, and the course argues that the obstacle was resources rather than ideas. Its answer is the meeting of generative AI, game engines that render in real time, virtual production and collaboration in the cloud.

You are asked to step into the role of a producer who can prove that this technology bridges a visionary story and a viable production, and to design a leaner model for a film that an independent producer or a mid-sized streaming platform could actually afford to make.

How the marks are split

The published teaching plan lists three assessed components: in-class exercises worth thirty per cent, a co-op assignment whose details come from industry partners worth thirty per cent, and a pitch worth forty per cent.

The pitch is then graded on four criteria: innovation and strategic thinking, producerial acumen, creative foundation with character design and world-building, and professionalism and clarity. Innovation carries the most weight, and the plan asks directly whether your workflow is genuinely new rather than the predictable one.

Notice what that means in practice: a lovely story with a vague technology plan cannot score well, and neither can a list of fashionable tools attached to no particular story.

AI for Creatives: Tools and Shifting Creative Roles

The opening lecture builds the working vocabulary: artificial intelligence as the broad field, machine learning as learning patterns from data, deep learning as layered neural networks, and generative AI as the family that produces new text, images, audio and video.

It then asks how these tools change creative work.

The useful lens is that AI transforms tasks inside a job at very different rates, and that creative practice ranges from passive automation, where a prompt does almost everything, to genuine co-creation, where human and machine feed each other ideas.

Style Language and Prompt Craft

A guest lecture on design styles makes a claim that matters for every image you generate: a named style is a complete system shaped by culture, technology and artistic movements, and knowing that language gives you a precise way to direct an AI tool and to brief collaborators.

The chapter pairs it with a layered prompt workflow, from subject and style through format, mood, viewpoint and exclusions.

Story Archetypes, Plot Shapes and the Synopsis

Before technology comes story. The course revisits recurring story shapes plotted as fortune against time, the seven basic plots from rags to riches through tragedy and comedy, and the setup, confrontation and resolution structure.

The chapter turns these into the test a synopsis has to pass before it goes into a pitch deck.

Character Archetypes and the Character Sheet

Twelve character archetypes, each with a motto, a core desire, a greatest fear and a characteristic weakness, give you a way to design people whose choices follow from who they are.

The in-class character design exercise then asks for a character sheet good enough for a feature animation shown at a leading animation festival.

Film Language, Storyboards and Motion

Shot size, camera angle and camera movement each carry meaning, and the chapter treats them as decisions rather than decoration.

It also covers the storyboard sequence from idea to final review, and the technical settings, frame rate and aspect ratio among them, that decide how motion reads on screen.

The Tech-Enhanced Production Pipeline

The project brief names technologies for each stage: AI-assisted concept art, script analysis, virtual location scouting and previsualisation before the shoot; LED volumes, digital humans and on-set AI tools during it; AI-assisted editing, cloud workflows, AI-driven effects and colour, and generative audio afterwards.

The chapter maps each tool to the cost or logistical problem it removes, which is exactly what the budget top-sheet must later justify.

Style and Character Consistency in AI Film

A published study assigned in the pipeline lecture compares three ways of keeping a generated film consistent from shot to shot.

Prompting alone kept only about a fifth of frames stable, a single fine-tuned adapter roughly doubled that, and separate adapters for style and for the character raised it to around seven in ten.

The chapter explains why separating style from identity works.

Web3, Transmedia and Story Worlds

The web has moved from reading, to platforms, to the promise of ownership, and the lecture traces how value in games has shifted towards players.

Transmedia storytelling is then separated from its neighbours: multimedia tells one story in many forms on one channel, crossmedia one story across many channels, and transmedia builds one story universe told through many stories, forms and channels.

Copyright and Ethics of AI in Screen Work

Human authorship remains the bedrock of copyright, so a work generated entirely by AI from a prompt cannot be registered, while a hybrid work with substantial human creative input may be protected.

The chapter adds the ethical risks the course lists and the survey estimates of how many film, music and gaming jobs generative AI may disrupt.

Pitching a Tech-Enhanced Feature or Series

The final chapter works through the single consolidated PDF the pitch requires: a pitch deck of ten to fifteen slides, a one-page budget top-sheet with traditional and tech-enhanced columns, a production pipeline and technology strategy of three to five pages, and a proof-of-concept asset in which an AI-generated concept frame is compulsory.

Worked example · free

Write the budget line that proves a technology choice

Key terms

Generative AI

AI that produces new content, including text, images, audio and video, by learning the patterns in its training data and recombining them in response to an instruction.

Prompt

The instruction or question a user gives an AI model to guide what it generates. In image work a strong prompt names the subject, style, format and mood, and states what should be excluded.

LoRA

Low-Rank Adaptation, a lightweight way of fine-tuning a large generative model on a small set of images so that it reproduces a particular style or a particular character reliably.

Character Drift

The failure of a generated character to look like the same person from shot to shot, as clothing, age or facial features change between images.

Previsualisation

Planning a scene before it is shot by building a rough moving version of it, such as an animatic or a simple 3D blocking, so that camera, timing and effects can be tested cheaply.

LED Volume

A stage surrounded by large LED screens that display a real-time rendered environment behind the actors, so that backgrounds and lighting are captured in camera instead of added later over a green screen.

Virtual Location Scouting

Building and exploring a location as a 3D environment in a game engine so that a production can plan shots and judge a setting without travelling to it.

Transmedia Storytelling

Telling one story universe through many stories, forms and channels, where each platform contributes new story elements and the audience assembles the whole.

Logline

A single sentence that states who the story is about, what they want and what stands in their way. It is the first thing an investor-facing pitch deck must carry.

Budget Top-Sheet

A single page that summarises the budget. In this course it has two columns, a traditional estimate and a tech-enhanced estimate, with annotations that justify each key saving.

Proof-of-Concept Asset

A piece of work that demonstrates the proposed technical approach, such as an AI-generated concept frame, a short previs animatic or a screenshot of a virtual location.

Human Authorship

The requirement that a work must result from human creative expression to be protected by copyright. A work generated solely by AI from a prompt does not meet it.

AF7620 FAQ

Questions students ask in each AF7620 chapter

Will AI replace creative designers?

The lecture's evidence points the other way for now. Creative design is placed in the assisted band of transformation, the group of skills where AI speeds up and supports the work rather than taking it over, and only about one per cent of skills are shown as fully transformed. The same slides note that anyone hoping to replace a designer with a prompt must first describe exactly what they want, which is itself design work. Chapter 01 · AI for Creatives: Tools and Shifting Creative Roles

How long should an image prompt be?

Long enough to remove the guesses that matter and no longer. The course workflow treats the subject as the only required element and adds style, format, mood, viewpoint and exclusions only where they change the frame. A short prompt that names a specific subject and a defensible style usually outperforms a long list of adjectives, and some tools also cap prompt length. Chapter 02 · Style Language and Prompt Craft

Which plot type is best for an AI-assisted pitch?

No plot type is favoured by the marking. What matters is that the story and the production method strengthen each other, which the brief calls synergy. A voyage and return story set in an impossible world can justify virtual environments, while a monster story can justify digital creatures; choose the plot first and let it tell you which technology is worth paying for. Chapter 03 · Story Archetypes, Plot Shapes and the Synopsis

How many archetypes should a single character have?

One should dominate, because a clear core desire and fear make choices predictable enough for an audience to follow. Secondary traits from another archetype can add depth, but when two archetypes pull equally the character's decisions start to look arbitrary, which is exactly what the lecture's list of desires and fears is meant to prevent. Chapter 04 · Character Archetypes and the Character Sheet

What frame rate should a pitch animatic use?

Film is traditionally shot at twenty-four frames per second, which is the rate the lecture lists for cinema, so an animatic meant to preview a feature should match it. Online video is often thirty frames per second and games sixty; mixing rates inside one animatic produces uneven motion that a reviewer will notice. Chapter 05 · Film Language, Storyboards and Motion

Do I need to use tools from every stage?

The brief asks you to address how technology changes each stage of the pipeline, so pre-production, production and post-production all need a considered answer. That does not mean using every tool; it means explaining for each stage which technology you chose, why, and how it saves cost or improves the film, including stages where you deliberately keep a traditional method. Chapter 06 · The Tech-Enhanced Production Pipeline

What is the difference between a style adapter and a character adapter?

A style adapter learns how things look, such as a watercolour texture or a dream-like palette, and applies that to any subject. A character adapter learns who one character is, their face, build and costume, so that the same person appears in any scene. The study found that training them separately worked where one mixed adapter failed. Chapter 07 · Style and Character Consistency in AI Film

Is transmedia just marketing?

No, and the lecture's definitions show why. Marketing and crossmedia repeat one story across channels to promote it, while transmedia gives every platform story found nowhere else, and the pieces add up to one world. A trailer on social media is promotion; a prequel comic that reveals a character's past is transmedia. Chapter 08 · Web3, Transmedia and Story Worlds

Can I copyright a film made entirely with AI?

Under the United States position described in the lecture, no: a work generated solely by AI from prompts lacks the human authorship copyright requires, and the courts upheld that in the Thaler case. A film in which people wrote the script, directed the choices, arranged and edited the generated material and added their own work is a hybrid, and its human contributions can be protected case by case. Chapter 09 · Copyright and Ethics of AI in Screen Work

Can the pitch be done individually or in a group?

The requirements are headed individual or group, and the proof-of-concept rules distinguish the two: the AI-generated concept frame is compulsory for both, while the short previs and the virtual location screenshot are optional for individuals. The teaching plan itself names the final week of presentations as individual project pitching, so confirm your arrangement with the teaching team. Chapter 10 · Pitching a Tech-Enhanced Feature or Series

How to prepare for the assessments

Choose your pitch genre in the first weeks and keep a one-page production log beside every lecture: the scene that costs most, the technology that could change that cost, and the prompt or tool you tried. By the pitch you will have the evidence the budget annotations and the pipeline strategy need, instead of reconstructing it in the final week.

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