Gen AI: Where design meets disruption

At first glance, a design university might not seem like ground zero for AI innovation. But look closer, and you’ll find a radical rethink in progress. At the World University of Design, the philosophy isn’t about resisting change, it’s about using it, shaping it, and scaling creativity through it. AI isn't just accepted here, it's being embedded into the DNA of the curriculum, from animation and game design to fashion and music.

In a conversation with Dr. Sanjay Gupta, Vice Chancellor, World University of Design, it becomes clear that this isn’t a tech institute trying to catch up. It’s a design powerhouse choosing to lead.

Teaching creativity in a tech-first world

The university’s approach begins with a crucial distinction: it’s not producing techies. It’s producing designers who understand tech, not just as users but as visionaries. That subtle shift changes everything.

Game design students, for instance, aren’t just taught how to use a 3D engine or animate a character. They start with board games, learning game logic, player psychology, and interaction design. From there, they move into animation, stop motion, digital environments, storyboarding, and level development.

Each student finds their niche. Some excel at character design. Others gravitate toward scenario building. Internships and graduation projects then match those interests with industry needs. It’s a fluid, layered process, powered by storytelling, design thinking, and increasingly, AI.

Gen AI isn’t the enemy; it’s a co-creator

Artificial Intelligence, especially Generative AI (GenAI), is often met with suspicion in creative fields. Here, it’s welcomed with open arms.

The first step? Remove the fear. Since 2019, all students including those in non-tech disciplines like fashion, visual arts, and music, have taken foundational courses in AI. The idea isn’t to turn them into data scientists. It’s to make them comfortable with tools that think.

Students are encouraged to explore emerging platforms as they launch, with the understanding that today's tool may be obsolete next week. The emphasis is not on mastering one software but on building adaptability, and seeing AI as a creative collaborator, not a threat.

When speed meets scale, expectations rise

Creative timelines that once stretched over weeks now collapse into minutes. What used to take 30 days can now be done in 30 minutes or less. That’s not an excuse to lower standards. If anything, it raises the bar.

With GenAI handling repetitive groundwork, students are expected to go deeper explore alternate narratives, improve fidelity, add complexity. Faculty members, too, are adapting. They're evolving from content deliverers to curators of critical thinking, pushing students to layer insight on top of automation.

One professor generates 100% of his visual content through AI, not only using it but teaching students how to create with it ethically.

Ethics isn't optional, it's the foundation

In a world where AI can generate, imitate, and replicate at breakneck speed, questions of ownership and fairness loom large. The university treats AI ethics as non-negotiable. Models trained on uncredited data without consent are labeled plainly: theft. Creators deserve attribution. If a system was built using questionable data, it shouldn't be celebrated, it should be scrutinized.

This stance reflects not just legal compliance, but a deeper creative integrity. Students are taught to build responsibly, with an understanding of what it means to create in a world where anything can be copied, but not everything should be.

Visualization is the new superpower

The biggest challenge in using AI effectively isn't technical. It's imaginative. Tech support is everywhere. APIs are easy. But knowing what to build, what’s worth solving, how a concept becomes a product, where a design can add value, that’s the hard part.

Students are trained to visualize use cases: where the data comes from, what the AI will do, and how it impacts the user. They might not be building machine learning models from scratch, but they understand how to structure data flows, test simulations, and define AI behavior in real-world contexts.

That act of visualization, the ability to “see the unseen,” is what differentiates an average user from a meaningful innovator.

Changing classrooms, changing mindsets

Faculty buy-in matters and so does faculty upskilling. Some professors adapt quickly, others take time. But across the board, there’s encouragement to integrate AI into everyday academic life. Assignments are evolving. Presentation decks use AI visuals. Research ideas are generated through prompt engineering.

Even assessment models are changing. When AI can complete a basic assignment, professors are rethinking how to evaluate creativity, originality, and depth. The bar has shifted. AI isn’t a shortcut, it’s the new starting point.

The CAD déjà vu

There’s a sense of déjà vu in this transformation. The same disruption happened decades ago when Computer-Aided Design (CAD) entered the classroom. Back then, traditional tools gave way to digital sketching. What took hours was done in seconds. Resistance existed—but so did evolution.

AI is following that same path. The speed may be faster, but the lesson is the same: adapt or fall behind.

Just as industry once pushed its workers to learn CAD, it's now nudging and sometimes demanding that talent embrace AI. The students coming out of this institution aren’t just ready. They’re already building what comes next.

When design leads the future

In a world chasing AI breakthroughs, it’s easy to forget the role of design the interface between tech and humanity. But here, design isn’t playing catch-up. It’s leading the integration, shaping how AI is used, visualized, and embedded into culture and creativity. Students aren’t taught to fear change, they’re taught to master it.