Hey Fellow Case Writers!

Over a thousand days ago, I wrote my first lines of code with AI. I know - code come on Matt but…. stick with me on this story.

In three days, I accomplished what might have taken me three months. I was amazed until I read what I'd made. The efficiency was undeniable. But when I looked at the code, I realized something was missing: my own voice.

The same experience happened when I started experimenting with AI for writing aspects of cases. This was over 2 years ago and I reflected on the absence of my voice in those words.

That unsettling moment changed how I think about teaching, writing, and what it means to be an author in the age of AI. If you're an educator or case writer today, you're probably facing this same question:

If a machine can generate polished narratives, what does it mean to author one?

Can that be authorship or what has to be true for authorship to happen?

From Author to Epistemic Designer

Case writing has always been a human driven process, a way to turn lived experience into teachable insight. Before AI, we were already curators:

• gathering facts

• choosing which voices to include

• shaping how students would learn from complex situations.

But something fundamental has shifted. Authorship/Writing today has a new way adjacent to what we have known to be true, isn't just about producing words, it's about designing meaning.

I've come to see the case writer's evolution as moving through distinct roles: Field Researcher → Author → Curator → Epistemic Designer. This progression reflects a shift from creating stories to architecting understanding itself.

In this new era, I believe authorship must be redefined …..NOT as invention from nothing, but as agency: the act of directing, interpreting, and standing behind what meaning is made.

The Curated Authorship Model

Over the past few years, I've been developing what I call the Curated Authorship Model (CAM Framework). It's not a rulebook, think of it as a compass for navigating AI-assisted work while maintaining your authorial integrity.

The model unfolds through four human-centered phases:

1. AI Narrative Generation – The machine produces predictive text: language without lived intent.

2. Human Curation – You act as an epistemic filter, selecting what is true, relevant, and meaningful.

3. Collaborative Iteration – Human and AI refine the story together, deepening understanding through dialogue and human lived meaning / experience.

4. Ethical Accountability – You disclose, contextualize, and stand behind the result.

The core truth is simple: Curation is authorship.

In an AI-mediated process, we move from being the source of words to being the source of meaning. This happens because of the sheer access to language that natural language models such as ChatGPT and Claude offer us. That accessibility needs to be validated and/or checked for meaning and intent.

We become both the epistemic filter, asking Is this true? and the narrative ethicist asking Is this right?

Why AI Text Is Hollow

Here's what I've learned: AI-generated language is not authored. It's statistical prediction, a reflection of patterns in human data, not a product of lived understanding.

The text it produces is syntactically coherent but semantically hollow. It's a mirror without consciousness.

AI can generate what I call synthetic novelty; new combinations of existing patterns. But it cannot create epistemic originality; new lived meaning. That's why authorship still matters: only a human can transform a reflection into truth.

Global copyright law affirms this. Across the U.S., U.K., and E.U., authorship is legally and ethically bound to the human mind because only humans can intend, interpret, and be accountable.

At least at this point.

The Human Imperative

AI offers unprecedented access and with it, a profound paradox. The more we gain access, the more we risk losing awareness.

To remain intentional in my work with AI, I've started using what I call the 4Ws Ethical Framework. Before publishing anything created with AI assistance, I ask myself:

1. What am I gaining? (Speed? Clarity? Inspiration?)

2. What am I losing? (My voice? Curiosity? Connection to sources?)

3. What am I neglecting? (Listening, observing, interviewing?)

4. What am I accessing? (New mastery—or borrowed skill?)

AI is not a substitute for authorship, it's a mirror that demands reflection.

Faster Cases or Truer Cases?

AI has democratized writing. Everyone can now produce words.

But few still mean.

The case method has never been about efficiency, it's about truth. And truth demands accountability: for the people we represent, the contexts we interpret, and the meaning we make.

I believe the future of case writing will not depend on faster cases, but truer ones, authored not by machines, but by curators of meaning who bring lived experience back to the center of learning.

The Choice Before Us

The Curated Authorship Model reminds us of something essential:

As educators, we're at a crossroads. We can let AI accelerate our work at the cost of our voice or we can learn to wield it as a tool that amplifies our intentionality without replacing our judgment.

I choose the latter. And I think the students we serve deserve nothing less.

What's your experience been with AI in your teaching or writing? I'd love to hear how you're navigating this shift—the gains, the losses, the questions you're wrestling with.

Reply to this email or join the conversation with me on LinkedIn. 

Let's reimagine authorship together.

— Matthew

Other Resources and Books

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Wrapping it all Up!

If you are interested in trying out the Free Case Study Prompt Generator .. Click the image below.

Thank you for being a part of our community. Together, we're shaping the future of education, one case study at a time!

Warm regards,

Matthew