Story generation, prompting for narrative, and the mechanics of AI creativity.
In 2023, the Writers Guild of America went on strike — one of their core demands was protection against studios using AI to generate scripts or to rewrite human-written scripts at lower cost. The writers weren't saying AI couldn't write. They were saying it was writing, and that raised questions about credit, pay, and authorship that nobody had answered yet.
Language models generate stories by predicting the statistically likely next token given prior context. They have no intent, no lived experience, and no emotional investment in the narrative. But they have processed enormous amounts of human storytelling — and that shows in the output.
The quality of AI storytelling correlates directly with prompt quality. Effective narrative prompts specify: protagonist with a concrete goal, an obstacle that creates genuine tension, a constraint that limits easy solutions, and a specific tone or voice.
Vague prompt: "Write a story about a girl." Specific prompt: "Write a story in the voice of a tired 1940s detective about a girl who discovers her school librarian is running a counterfeit book operation. Dry humor, noir tone, first person." The second produces something distinctly different.
4 questions — free, untracked, retake anytime.
is the main limitation of AI-generated narrative compared to human writing?
did the WGA strike include AI as a core concern?
makes a narrative prompt more effective?
voice in storytelling is best described as:
Analyze and strengthen a story prompt.
The AI guide will help you analyze what makes a narrative prompt work and workshop one of your own.
Co-authorship models, improv with AI, and the dynamics of turn-taking narrative.
Improv theater has a golden rule: "yes, and..." Whatever your scene partner adds, you accept it as real and build on it. You don't block, redirect, or deny — you expand. Writers working with AI report that the most interesting results come from applying the same principle: accepting what the AI generates and asking yourself, "where could this go?"
Human-AI creative collaboration takes several forms:
AI excels at execution within established patterns. Humans bring what isn't in the training data: lived experience, emotional specificity, cultural particularity, genuine surprise. The most interesting co-authored work comes from human directions that force the AI off its most statistically likely path.
Use AI for bulk, pattern, and execution. Use yourself for direction, surprise, and meaning. The story is better when each party does what it's actually good at.
4 questions — free, untracked, retake anytime.
the 'yes, and...' improv rule, what does 'yes' mean?
type of co-authorship is happening when you write an outline and AI expands it into full prose?
do humans bring to co-authorship that AI genuinely cannot?
does AI produce the most interesting collaborative output?
Build a turn-taking story using yes, and... thinking.
Build a story using turn-taking with your AI guide. Apply "yes, and..." thinking throughout.
Image and audio generation — how it works, what it produces, and what it means.
Midjourney, DALL-E, and Stable Diffusion entered public use in 2022-23 and immediately won art competitions, illustrated books, and generated album covers — sometimes without disclosure. In August 2022, "Théâtre D'Opéra Spatial" by Jason Allen, generated with Midjourney, won first place at the Colorado State Fair fine arts competition. Allen disclosed he used AI. The result sparked a global debate about whether AI-generated images constitute art.
Image generation models (like Stable Diffusion) use a process called diffusion: they start with random noise and gradually remove that noise in a direction guided by a text prompt, until a coherent image emerges. They were trained on billions of image-caption pairs from the internet.
These models were trained on images created by human artists — often without consent or compensation. This has led to lawsuits, licensing debates, and ongoing questions about what "learning" from creative work means legally and ethically.
If a model trained on ten thousand paintings by a specific artist can generate images "in the style of" that artist, and sells those images commercially — what does the original artist deserve?
4 questions — free, untracked, retake anytime.
do diffusion models generate images?
Colorado State Fair AI art controversy raised what core question?
is the key ethical concern about training data for image generation models?
transfer in AI image models means:
Analyze the ethics of training on artists' work.
Discuss the training data ethics question with your AI guide.
Deepfakes, authenticity, and synthetic media literacy.
In 2023, a synthetic image of an explosion near the Pentagon went viral, briefly sending stock markets down before being identified as AI-generated. No explosion had occurred. The image had been shared by verified Twitter accounts. It took less than an hour to cause measurable financial impact before fact-checkers caught up.
Deepfakes use AI (specifically GANs — Generative Adversarial Networks) to create convincing synthetic video or audio of real people. The technology started as a research curiosity and is now widely accessible.
Detecting synthetic media is increasingly difficult as tools improve. More reliable than technical detection is source verification:
Synthetic media has effectively ended the era in which "seeing is believing." Verification now requires sourcing, not just perception.
4 questions — free, untracked, retake anytime.
made the synthetic Pentagon explosion image particularly dangerous?
'voice deepfake' can be created from:
is source verification more reliable than technical detection for synthetic media?
key question does synthetic media raise about the phrase 'seeing is believing'?
Build your personal verification protocol.
Build your personal verification protocol for synthetic media.
Copyright, authorship, and creative credit in the age of AI.
In 2023, the US Copyright Office ruled that a graphic novel created with Midjourney images could not be copyrighted — because copyright requires human authorship. The comic's creator, Kristina Kashtanova, retained copyright for the text and arrangement, but not the AI-generated images themselves. Meanwhile, authors including George R.R. Martin and John Grisham sued OpenAI, alleging their books were used to train ChatGPT without permission.
Current US copyright law requires human authorship. AI-generated content — images, text, music — cannot be copyrighted as of 2023 rulings. This creates a paradox: anyone can use, copy, and sell AI-generated content freely.
The second legal battleground: were AI models trained on copyrighted works without permission? Current lawsuits challenge whether training on copyrighted text and images constitutes infringement — or fair use. No definitive ruling has been made in most jurisdictions.
If an AI model trained on your creative work can produce outputs that compete commercially with your own work, what legal and ethical obligations does the model's creator have to you?
4 questions — free, untracked, retake anytime.
can't pure AI-generated images be copyrighted under current US law?
did the Copyright Office allow Kristina Kashtanova to copyright in her AI-illustrated comic?
is the core legal claim in author lawsuits against AI companies like OpenAI?
is the practical consequence of AI outputs having no copyright protection?
Analyze authorship and copyright for AI creative work.
Analyze the authorship and training data questions with your AI guide.
Collaboration models, augmentation vs. replacement, and the future of creative work.
The band Radiohead's producer Nigel Godrich, the composer Hans Zimmer, and novelist Kazuo Ishiguro have all spoken publicly about using AI in their creative process — not to replace creativity, but to get unstuck, generate raw material, or hear unexpected possibilities. At the same time, entry-level illustrators, stock photo contributors, and junior copywriters have seen their markets collapse as AI tools perform similar work at near-zero cost.
AI creative tools can augment human creativity — expanding what's possible for a skilled human — or replace it, substituting AI output for human work at lower cost. The same technology does both simultaneously, for different people:
The most productive human-AI creative relationships typically follow a pattern: humans provide direction, surprise, meaning, and specificity; AI provides scale, variation, and execution. The challenge is maintaining the human contribution rather than deferring to AI defaults.
When humans consistently accept AI's first suggestions rather than pushing back, the creative output converges on the statistical average of the training data. The distinctive voice gets averaged out.
4 questions — free, untracked, retake anytime.
is the difference between AI augmenting and AI replacing creative work?
of these is an example of AI augmentation (not replacement)?
is the main risk when humans consistently accept AI's first creative suggestions?
do humans bring to AI collaboration that AI cannot replicate?
Develop your framework for human-AI creative collaboration.
Develop your own framework for human-AI creative collaboration.
6 questions covering all lessons. Free, untracked, retake anytime.
makes AI story generation potentially formulaic?
human-AI creative collaboration, the most interesting output comes from:
models generate images by:
can't pure AI-generated images be copyrighted under current US law?
key difference between augmentation and replacement in AI creative work is:
media literacy primarily means: