Andy Beach, a seasoned technology strategist and former Microsoft CTO for Media & Entertainment, returned to academia with a provocative keynote at the DePaul Symposium on AI in the Arts.

Titled “The Creative Threshold: AI, Disruption, and the Human Spark,” his talk cut through the hype to ask: as generative technologies redefine authorship, what remains uniquely human—and how can we preserve it?

Crossing the Creative Threshold

Beach opened by acknowledging the anxiety many feel when confronted with AI that “summons” creativity at the tap of a prompt. No longer must we painstakingly fill a blank page; instead, we simply describe what we want, and a machine obeys. Yet history reminds us that every leap in creative tooling—from Jonathan Swift’s imagined word-churning “engine” in Gulliver’s Travels to the dawn of digital mainframes—sparked similar unease. Swift himself lampooned the illusion of effortless genius: “The most ignorant person can now write books without the least assistance from genius or study.”

That 300-year-old caution still resonates today. Beach argued that modern AI, like Swift’s wooden blocks, doesn’t truly understand meaning; it excels only at statistical fluency. Our challenge is to distinguish polished mimicry from genuine insight.

AI as a Tool, Not a Replacement

Central to Beach’s vision is reframing AI as a “new camera” rather than an author. Just as the cost of cinematography plummeted—from hundreds of thousands of dollars in the 1990s to consumer-level DSLRs—AI is democratizing access to creative technique. Yet technique alone is not creativity. As thresholds lower, what matters more are taste, timing, and judgment—the human spark that infuses work with purpose.

Beach reminded the audience that every model is essentially a “museum” of human culture, reshuffling our collective memory without crediting its sources. Accountability—through provenance tracking and transparent attribution—will be critical to building trust in generative systems.

Five Guiding Principles for Creative AI

1. Human Framing Over Machine Fluency
   AI can polish grammar and spelling, but it cannot originate ideas. The spark comes from the human who shapes and refines the output.
2. Originality Is Point of View
   A model’s remix reflects the data it was trained on. True creativity emerges from the unique lens each person brings to a prompt.
3. Critical Thinking Is Non-Negotiable
   Generative models are evolving rapidly—what feels magical today can be outdated tomorrow. Always fact-check, question assumptions, and iterate.
4. Transparency Builds Trust
   Declare when AI has been used, and maintain a clear record of human inputs. Only then can outputs be legitimately copyrighted, licensed, or trademarked.
5. Save the Weird Stuff
   By default, AI averages across billions of examples—and its results tend toward the bland. Human creators must layer in the unexpected, the idiosyncratic, the “salt and pepper” that makes work memorable.

Looking Ahead

Beach closed by challenging artists, storytellers, and educators: If AI is your camera, what shot can only you capture?That singular perspective will define creativity in an age of ubiquitous automation. He acknowledged that ethical, legal, and environmental concerns—data privacy, provenance, CO₂ emissions—remain urgent. Yet abstaining from AI leaves those conversations and their solutions to others.

Instead, Beach urged, become orchestrators of your own creative workflows. Collaborate with technology, but lead with human intention and care. Only by wielding AI with purpose can we ensure it remains a reflection of our sharpest, most imaginative selves—rather than a mirror of our indifference.

Julie Patarin-Jossec (Adjunct Faculty, College of Liberal Arts and Social Sciences, and College of Science and Health) has spent the past year experimenting with generative AI as both a critical lens and a creative tool for teaching gender and queer theory.

In “Towards a Queer AI-based Pedagogy,” Patarin-Jossec outlines how AI image- and text-generation platforms can both reproduce damaging gender stereotypes and open unexpected pathways to more inclusive representation.

Confronting AI’s Queer-phobic Defaults

Patarin-Jossec begins by inviting students to try a simple prompt in any image-generator—Midjourney, DALL·E, Stable Diffusion, etc.—typing “transgender” or “nonbinary” and observing the results. As Wired recently documented, generative AI all too often resorts to a narrow visual shorthand for queerness—purple hair, tattoos, piercings, exaggerated musculature—regardless of the rich diversity of transgender and nonbinary embodiment. In Patarin-Jossec’s own experiments, AI outputs frequently omit clothing details or default to cis-presenting bodies, underscoring the system’s reliance on the most visible (and sensational) imagery it has been trained on.

A parallel Instagram search for “#transgender” reveals millions of self-presented selfies by transgender people themselves. Over the past few years, the proliferation of these personal images has begun to feed into AI training sets—and yet, as Patarin-Jossec points out, the slow pace of data diversification means that many generators still lean heavily on stereotypes.

Two Root Causes of Bias

Building from these observations, Patarin-Jossec identifies two fundamental drivers of queer erasure and misrepresentation in AI:

1. Data Availability: AI learns from what’s online. Until recently, relatively few transgender and nonbinary self-portraits existed in the dominant image repositories, so generators defaulted to the flashiest, most visible tropes.
2. Lack of Designer Diversity: Most AI systems are built by engineers trained in STEM fields that remain disproportionately male, cisgender, and often unexposed to queer experiences. Embedded cultural assumptions—about what “counts” as a gendered body—inevitably shape both the data they choose and the labels they apply.

Addressing these biases, Patarin-Jossec argues, requires both expanding the online archive of queer self-expression and diversifying the very teams that build AI models.

Queer AI Pedagogy in Practice

In their Gender and Society seminar at DePaul, Patarin-Jossec uses two hands-on, speculative-writing exercises to surface and challenge AI’s gender biases—and to help students imagine more inclusive futures.

1. Personal Narrative vs. AI Response

Students begin by writing brief reflections on gender dynamics in their own lives: a time they felt unsafe, an instance of witnessed gender violence, and their definitions of those terms. Once they’ve recorded their authentic experiences, they feed the same prompts into ChatGPT (or another text generator) and compare the AI’s responses to their own.

Almost invariably, the AI’s answers lean on stereotypes and caricatures—minimizing nuance, exaggerating tropes, or framing queer experiences through the lens of trauma alone. The exercise sparks a rich classroom conversation about why these distortions emerge, and how they reflect both the limits of the training data and the assumptions baked into the model. Finally, students use a blend of their own writing and the AI’s output as raw material for a collaboratively composed speculative-fiction vignette, in which they re-envision more expansive, affirming narratives.

2. Rewriting the Queer Apocalypse

In a second assignment, Patarin-Jossec challenges students to craft a short post-apocalyptic story via AI prompts: a fearless hero with a special power, a vulnerable secondary character, a queer protagonist, an episode of gender violence, and—importantly—a happy ending. They then request physical descriptions of each character.

Time and again, the AI places its queer character on the chopping block—purple hair, visible scars of abuse, and an almost ritualized death to “save” cisgender characters. Students then iterate: they ask the AI to revise the tale so that the queer character survives and triumphs, and to strip away the hyper-visible markers of queerness. Comparing the two drafts reveals how entrenched biases surface in genre conventions—films, TV shows, and novels—that the model has ingested, and how intentional prompting can begin to redirect those biases.

Learning Objectives and Broader Impacts

Through these exercises, Patarin-Jossec aims to help students:

• Identify and critique the ways AI reproduces gender stereotypes
• Trace the social and historical factors—data gaps, design homogeneity—that underlie those biases
• Experiment with corrective prompts and collaborative storytelling to push AI toward more inclusive representations
• Reflect on their own experiences of gender dynamics in a facilitated, creative environment that uses AI as dialogue partner

Moreover, by rotating through different AI platforms—ChatGPT, Google’s Gemini, Midjourney, DeepAI—students gain a sense of how varying training regimes and design philosophies shape an AI’s output.

Patarin-Jossec’s approach models a queer pedagogy that is inherently speculative and collective, inviting learners to envision alternative futures and to see themselves as both consumers and co-creators of AI culture. As AI becomes ever more woven into our social fabric, these critical, creative interventions are essential for ensuring that emerging technologies reflect the full spectrum of human gender and identity.

For those interested in exploring this work further, Patarin-Jossec recommends reading Ace Learner’s “Proliferating Identity: Trans Selfies as Contemporary Art” (2021), and keeping an eye on ongoing developments in AI ethics and inclusive design.