Evaluating AI-Assisted Design
Is AI stealing designers’ thunder? ⚡️
❝The most dangerous phrase in the language is, ‘We’ve always done it this way.’❞
— Grace Hopper1
I’ve recently found myself often reflecting on how our industry is wrestling with the rise of AI in design. Demetrios Fakinos’ recent LinkedIn post sparked this conversation anew, setting off a lively debate: How should design awards treat work created with AI? Having served on the ED Awards digital jury many times, I became interested in this more specific question.
If you read my last newsletter, Designing in the Age of AI Agents, you’ll remember I argued that AI isn’t just another tool in the designer’s kit, certainly not like Photoshop or Figma. AI is more like a collaborator that’s already sitting in our meetings, suggesting changes, and occasionally making us question our own design sensibilities. We’re not just wielding AI; we’re negotiating with it, curating its outputs, and sometimes even arguing with its tone choices.
Three approaches for the future of Design Evaluation
The old question “What tools did you use?” feels old-school when the tool in question can generate, remix, and even critique creative work. The real dilemma is whether we’re judging the final result, the process, or the interplay between human and AI.
Here’s how it could go:
Contextual Transparency: Ask designers to openly document how AI contributed to their project. This isn’t about gatekeeping but about understanding the creative dialogue between humans and machines.
Dynamic, Impact-Driven Categories: We move beyond traditional award categories and introduce dynamic ones that spotlight the impact and innovation of each project rather than focusing on process or tools. These new categories recognise inventive thinking, societal contribution, and how effectively designers collaborate with AI as a creative partner.
Process-Focused Recognition: Why not celebrate both the outcome and the journey? Invite designers to share their “making-of” stories, highlighting the messy, fascinating collaboration between human intent and algorithmic suggestion.
1. Contextual Transparency (The Sensible Approach)
Mandatory Disclosure: Require all entrants to clearly document how AI was used in their projects. This includes specifying which tools were used, what parts of the work were AI-assisted, and how human creativity directed the process.
Judging on Merit: Maintain established design criteria (innovation, execution, impact, and aesthetic quality) regardless of the tools involved. This ensures fairness and keeps the focus on creative excellence.
Educational Value: These disclosures build a living archive of AI’s evolving role in design, helping both juries and the wider industry understand new practices and trends.
2. Dynamic, Impact-Driven Categories (The Radical Approach)
Category Reinvention: Move beyond traditional divisions and create award categories focusing on impact, innovation, and societal value. Examples might include “Regenerative Design,” “Cross-Cultural Innovation,” or “Systemic Impact.”
Celebrating Collaboration: Explicitly recognise human-AI collaboration as a new creative paradigm. Judge how effectively designers orchestrate AI tools to achieve meaningful outcomes.
Adaptive Evaluation: Use AI to analyse entry patterns and update categories annually, ensuring the awards stay relevant as technology and society evolve.
Cross-Disciplinary Juries: Expand juries to include ethicists, technologists, and community voices, ensuring a holistic evaluation of design’s broader implications.
3. Process-Focused Recognition (The Hybrid Approach)
Dual Evaluation: Introduce parallel awards for the final output and the creative process. This means recognising not only the result but also the ingenuity, strategy, and ethical considerations behind the work.
Transparent Narratives: Ask entrants to submit a process narrative or “making-of” dossier, detailing their creative journey, decision-making, and the interplay between human and AI contributions.
Ethics and Intent: Emphasise ethical design, originality, and responsible AI use. This approach rewards designers who push boundaries thoughtfully and transparently.
Mentorship and Learning: Use process-focused recognition as an educational tool, sharing best practices and inspiring the next generation of designers to engage critically with emerging technologies.
Implementation Strategies
For Contextual Transparency: Develop standardised disclosure forms, train judges to evaluate AI-assisted work, and pilot these practices in select categories.
For Dynamic Categories: Collaborate with technology partners and academia, create new metrics for societal impact, and build adaptive award systems.
For Process-Focused Recognition: Encourage entrants to document their creative journey, provide resources on ethical AI use, and celebrate both the “what” and the “how” of design.
AI: Not Just a Tool, But a Teammate (With Opinions)
Let’s be clear: AI is not just a faster, shinier version of what came before. It’s a new kind of creative presence that can nudge, surprise, and even challenge us. Treating it as “just another tool” misses the point.
The real shift is that designers must now curate, critique, and sometimes push back against AI’s suggestions. The skills that matter most, framing the right problem, exercising taste, and upholding ethics, are more critical than ever.
Looking Ahead (and a Little Sideways)
If the current debate is any indication, we’re still writing the rules. And honestly, that’s exciting. We have the chance to shape not just how we award design, but how we define creativity itself in an AI-augmented world.
And who knows? Maybe in a few years, we’ll see a panel of AI agents hotly debating whether to give credit to a human for “inspiring” a particularly clever layout. (“Do we mention the human’s contribution, or just say the prompt was unusually well-phrased?”)
Until then, let’s keep the conversation open, the disclosures honest, and the humour alive. The future of design (and its awards) will be shaped by how bravely we embrace the questions and the quirks of working alongside our new digital teammates.
NK
☉
Grace Hopper (1906–1992) was a pioneering American computer scientist and Navy rear admiral who developed the first compiler and was instrumental in creating programming languages like COBOL. Her work made it possible for people to interact with computers using more natural, human-like language—a breakthrough that laid the foundation for modern computing and, by extension, artificial intelligence, where accessible programming and human-computer interaction are key.