Am I a 'Writer'?

Why We Need New Language for AI-Assisted Creation

Over the last couple of weeks, I’ve been serializing my book The Detection Deception here on The Augmented Educator, using AI tools in my writing process. This is a fact I’ve disclosed in my ethics and AI statement. But as I prepare each installment, I find myself returning to a question that initially seemed simple but has revealed itself to be anything but:

What do I call myself in relation to my book?

Am I a “writer?” An “author?” These terms feel simultaneously accurate and inadequate. I conceive ideas, structure arguments, revise and refine. But I also prompt, curate, and collaborate with systems that generate text I never could have written alone. The traditional vocabulary of creation strains under this new reality.

This question matters more than semantics suggest. Throughout my work here, I’ve argued multiple times that we must shift the value proposition of creation from artifact to process—from the essay that appears on this blog to the thinking that produced it. If that’s true, then the term “writer” becomes crucial. It’s the word that locates the source of that process and identifies where creative labor happens, where meaning originates. To use it carelessly in an age of AI-assisted creation isn’t just sloppy, it’s potentially deceptive.

However, what truly stands out is this: like many AI-related issues, we’ve encountered this situation before. History is littered with moments when new tools forced creators to confront uncomfortable questions about their identity and the authenticity of their work. What can these historical parallels teach us about our current moment? And might they even suggest that we need not just to defend old terms but create new ones instead?

When the Printing Press Made Writers

Our current concept of authorship is itself a technological artifact. The concept of a “writer” or “author” as a sole originator of a fixed text didn’t emerge until the printing press was invented.

According to Elizabeth Eisenstein’s work on medieval scribal culture, a “writer” or “scribe” was perceived as a skilled worker, someone who channeled established knowledge into physical form. The concept of “originality” was largely absent; knowledge belonged to collective inheritance. Each manuscript, painstakingly copied by hand, existed in a fluid state, constantly evolving through copying. The scribe’s role was preservation and transmission, not innovation.

The printing press changed everything through standardization and dissemination. Identical copies could be distributed widely, and knowledge could be preserved without corruption. But most significantly, print culture created a fixed personality for the author, linking a specific work to a single creator’s name.

This shift directly gave birth to modern authorship, fostering new notions of individuality, originality, and creativity. It enabled the Romantic ideal of the “genius” as a creator whose work arises from internal inspiration rather than channeling tradition. The writer as originator, as unique creative voice, is not a timeless truth about human creativity. It’s a construct of print technology.

And if it was technology that transformed how we define a “writer,” then why do we think that definition is set in stone?

The Photographer Who Wasn’t an Artist

The advent of photography caused a crisis that was surprisingly like the one we’re experiencing now. Charles Baudelaire’s influential 1859 essay “The Modern Public and Photography” launched a fierce attack on the new medium, arguing it catered to base desires for “accuracy over beauty” and would corrupt art by enslaving artists to external reality. Photography should remain the “humble servant of the sciences and arts,” never a creative peer. Critics derided it as “thoughtless mechanism for replication” lacking “refined feeling and sentiment.”

Sounds familiar? It should. Concerns that AI just imitates and remixes, doesn’t have genuine emotions, and endangers original creativity are remarkably similar to the worries about cameras that arose in the 19th century.

But photography didn’t destroy painting. One could argue that it liberated it. By mastering mechanical reproduction, photography liberated painters from simply copying reality. Artists could explore subjectivity, emotion, abstraction—aspects cameras couldn’t capture. It was photography that facilitated Impressionism and modern art.

Walter Benjamin theorized this in “The Work of Art in the Age of Mechanical Reproduction,” introducing the concept of “aura,” the unique presence an original artwork possesses through its existence in a specific time and place. Mechanical reproduction destroys this aura, shifting art’s function from “cult value” (ritual and uniqueness) to “exhibition value” (accessibility and mass viewership). Benjamin saw both loss and democratizing possibility: reproduction made art accessible to the masses, transforming it into a basis for discourse.

The pattern: a technology condemned as inauthentic and mechanical forced radical re-evaluation of an entire field, redefining value itself. The “artist” didn’t disappear, but the term came to mean something fundamentally different after photography.

When Musicians Became Operators

The 20th century’s introduction of synthesizers and Digital Audio Workstations followed a similar trajectory. DAWs democratized music production, placing professional studio tools into amateur hands and enabling bedroom-created genres. A generation mastered their craft through experimentation and online tutorials, bypassing instrument lessons.

This sparked authenticity debates echoing our current AI controversies. Rock culture valued “real” instruments as direct extensions of the musician’s body and soul. Electronic instruments were dismissed as “false,” creating an alienating technological abomination. The concern centered on “liveness” as the idea that performance value derives from real-time, physical, potentially fallible human actions.

But electronic music didn’t destroy musicianship. It redefined it. The DAW blurred the lines between performer, composer, and audio engineer. A new figure emerged: the producer as the composer, whose primary instrument is the studio itself. “Musician” expanded to include those whose craft wasn’t playing traditional notes but building worlds of sound through technology. And virtuosity came to mean sophisticated sound design, arrangement, and masterful technology manipulation.

The Thread Running Through History

These cases reveal a consistent philosophical resistance to technological disruption. It’s a common criticism that new technologies lack genuineness, feel impersonal, or lack depth. They are often seen as tools for mere reproduction rather than true creation, as threats to the “human touch.”

But what actually happens is far more nuanced. Technologies that seem to mechanize creative work often redistribute the location of creative value. Photography freed visual artists to explore subjectivity, and synthesizers created new musicianship.

The crucial insight in both cases is the “liberation effect.” By handling mechanical or technical aspects, new technologies freed human creators to focus on higher-order capacities: deep synthesis, genuine originality, emotional resonance, and philosophical inquiry. The technology didn’t just change what’s new; it redefined the purpose and value of what’s old.

The Migration of the Aura

Returning to Benjamin’s concept of “aura” provides a useful framework for understanding our current moment. If we accept AI represents the ultimate agent in the decay of the artifact’s aura, being capable of producing infinite variations, each detached from a singular physical origin, then where does authenticity actually live?

I would argue that the aura doesn’t vanish; it migrates from the artifact to the process, from the output to the human intention and judgment that shapes it. In the age of AI-assisted creation, a new form of authenticity emerges, founded on three pillars:

• The aura of intent. The value and authenticity of a work become rooted in the sincerity, originality, and significance of the human vision or question that initiated the creative process. This is where the “why” resides. It is the purpose, the meaning, the point that the creator is trying to make or explore.

• The aura of process. Authenticity is demonstrated through transparency about method. In my own work, disclosing my use of AI isn’t a confession of diminished creativity. Instead, it’s a claim to a new form of craftsmanship and intellectual honesty. It says: this is how this was made, and I stand behind both the process and the product.

• The aura of curation. Value is located in the unique human judgment, aesthetic sensibility, and critical thinking demonstrated in selecting, refining, and synthesizing AI-generated materials into a coherent final work. Anyone can prompt an AI to generate text. Not everyone can recognize which outputs are worth keeping, how to arrange them meaningfully, what’s missing, what needs to be rewritten entirely in a human voice.

This represents a fundamental shift in where creative labor happens. The traditional model flows from human intent through human skill to create an artifact. The AI-augmented model reconfigures this: human intent is expressed, a technological process generates options, and human critical and curatorial skill produces the final artifact. “Craft” now involves both initial conceptualization and final critical evaluation, rather than just skillful execution.

What Do We Call This?

So here’s where we arrive: if the historical pattern suggests that technological disruption tends to relocate rather than eliminate creative value, and if that value in our current moment has shifted from artifact to process, from output to intention and curation, then what do we call the person who does this work?

“Writer” and “author” are terms forged in the age of print, designed to describe a particular relationship between an individual creator and a fixed text. They carry centuries of accumulated meaning about originality, ownership, and the singular vision of a creative genius. These terms aren’t wrong in connection with AI-assisted craftsmanship, but they may no longer be sufficient or precise enough.

This matters because language shapes how we think about value. When we use traditional terms to describe AI-assisted work without qualification, we risk either overvaluing the human contribution (claiming full authorship of text we didn’t entirely write) or undervaluing it (suggesting that AI-assistance means the work isn’t “really” ours). Neither reflects the actual nature of the creative partnership.

Perhaps we need new terminology that more accurately captures the nature of AI-assisted creation. Some possibilities that have been proposed or are emerging:

• AI-Assisted Creator (Writer, Artist, Musician): This might be appropriate when a human provides the core concept, establishes creative direction, and performs significant curation, editing, and refinement of AI-generated content. The AI functions as a sophisticated tool for brainstorming, drafting, or generating elements, but the final work is substantively shaped by human vision and labor.

• AI Curator or AI Director: These terms might better fit scenarios where the human’s primary contribution is prompting and selecting from largely finished outputs, with minimal subsequent modification. Here, the creative act is almost entirely one of selection, arrangement, and presentation, analogous to how a museum curator creates meaning through the thoughtful arrangement of objects they didn’t create.

• Augmented Writer: This term acknowledges both the human core and the technological enhancement. It suggests that the fundamental creative act remains human while being amplified or extended through AI tools, similar to how we might speak of augmented reality as adding layers to rather than replacing physical reality.

The challenge is that none of these terms feels quite right yet. They’re too new, too clinical, too explicitly technological. They lack the weight and simplicity of “writer” or “author.” But perhaps that discomfort is precisely the point. Perhaps we need terms that make the nature of the collaboration visible, and that force both creators and audiences to grapple with how the work was actually made.

The Case for Specificity

What’s at stake in this question of naming isn’t just professional identity or credit; it’s the preservation of concepts that matter deeply. If we allow the term “writer” to encompass everything from someone who thoughtfully collaborates with AI to refine their own ideas to someone who simply inputs prompts and copies outputs verbatim, we risk making the term meaningless. We lose the ability to distinguish between different types of creative labor.

Consider a parallel from another field: we don’t use the same term for someone who designs a building and someone who constructs it according to plans, even though both are essential to creating architecture. We recognize that “architect” and “builder” describe different types of expertise and contribution. Perhaps we need similar distinctions for AI-assisted creative work.

The urgency of developing this vocabulary is practical as well as philosophical. In academic contexts, we need clear language to discuss what makes up acceptable use of AI-assistance versus academic dishonesty. In professional contexts, we need ways to accurately represent our skills and contributions. And in artistic contexts, we need terms that allow meaningful critical discourse about how work was created and what that means for its interpretation and value.

More fundamentally, however, as I’ve argued throughout my work on this blog, education’s core mission in the age of AI must be to develop distinctly human capacities that cannot be easily automated. If we’re going to successfully advocate for this process-centered, human-capability-focused approach to learning, we need language that makes the nature and value of human creative processes visible, including those that involve AI collaboration.

The Ethical Imperative of Transparency

Whatever terminology we eventually settle on, one principle seems clear: transparency about process must be central to any ethical use of AI in creative work. In a world where the aura has migrated from artifact to process, disclosing that process isn’t optional; it’s the very foundation of authenticity.

This is why I maintain my ethics and AI disclosure statement. It’s not because I’m uncertain about whether my work “counts” as writing. It’s because radical transparency about method is now the primary way creators show intellectual honesty. In previous eras, authenticity might have been demonstrated by the uniqueness of the physical artifact or the virtuosity of technical execution. Today, it’s shown through an honest accounting of how the work came to be.

This transparency serves multiple purposes. It builds trust with audiences, models responsible AI use for others, particularly for students and educators, and creates accountability. I’m declaring not just that I used AI but how I used it, which means others can evaluate whether my claims about my creative process are credible. Crucially, it contributes to the broader cultural effort of establishing guidelines for AI-driven creativity.

The alternative, which is claiming traditional authorship while quietly using AI, or conversely, hiding human creative contributions behind AI generation, creates exactly the confusion and erosion of trust that threatens the creative professions. If we want terms like “writer” to continue to mean something valuable, we need to be honest about when and how they apply to our work.

A Question Worth Grappling With

As I continue publishing The Detection Deception and other work created through human-AI collaboration, I find myself still uncertain about the right terminology. “Writer” feels both true and insufficient. “AI-assisted writer” is more accurate but awkward. Perhaps I’m a “curator of AI-augmented text.” Or maybe I’m simply doing something that doesn’t yet have an adequate name.

What I’m increasingly convinced of is that this linguistic uncertainty is productive rather than problematic. It keeps me honest about my process. It prevents me from slipping into either of two equally misleading positions: that AI-assistance means my work isn’t “real” writing, or that my creative contribution is identical to traditional solo writing just because both result in text under my byline.

The historical record suggests that we’re in a transitional moment. It is not the first of its kind, but perhaps more rapid and more fundamental than previous technological shifts. If the pattern holds, we’ll eventually develop new vocabulary and new norms that accurately reflect the changed nature of creative work. The question is whether that vocabulary will emerge thoughtfully, through careful consideration of what we value and want to preserve, or whether it will simply be imposed by technological momentum and market forces.

This strikes me as the crucial work for those of us creating with AI in 2025: not to defend the old terms against all change, not to uncritically embrace whatever new language tech companies or AI advocates promote, but to actively participate in the construction of terminology that serves human progress. We need language that preserves the uniqueness and value of human creative processes while acknowledging the reality of technological collaboration. We need terms that make meaningful distinctions visible rather than obscuring them.

So here’s my question for fellow educators, writers, and creators wrestling with these issues: Do we need new language for AI-assisted creation specifically to preserve “writer” and equivalent terms for work that emerges primarily from human creative processes? Should we develop a distinct vocabulary that acknowledges AI collaboration as a fundamentally different mode of creation—not better or worse, but different enough to warrant different terms? Or would doing so create artificial boundaries that don’t reflect the reality of how creativity actually works in an age of augmentation?

I don’t have a definitive answer. But I’m convinced the question matters, and that getting the language right—or at least, more right than it currently is—will shape how we navigate the transition from product-centered to process-centered understandings of creative value. In an era when AI can generate plausible artifacts with minimal human input, the terms we use to describe human creative labor may be one of the most important things we have left to protect.

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How are you describing your own AI-assisted creative work? Have you found terminology that feels both honest and adequate? For educators: what language are you using with students to distinguish between different levels of AI collaboration? Do we need new terms to preserve the meaning of “writer” and “author,” or should these words evolve to encompass AI-assisted creation? Share your vocabulary experiments and uncertainties in the comments.

P.S. I believe transparency builds the trust that AI detection systems fail to enforce. That’s why I’ve published an ethics and AI disclosure statement, which outlines how I integrate AI tools into my intellectual work.