AIl change in the GenAI studio

The rapid advancement and application of GenAI in design education are in full view at the start of the new 2026/27 academic year, across disciplines and year-levels. Although GenAI may overwhelm students with too much choice, choice that, moreover, can be difficult to verify, GenAI tools, such as ChatGPT and  Midjourney, have become ubiquitous in and out of studio environments. And so, GenAI tools typically help students to generate ideas and spend less time on repetitive tasks like drafting, organising research data or checking design quality before traditional studio critique. But GenAI raises as many questions as they answer. For example, students are known to get generic feedback from tutors using GenAI rather than specifically written for one person. Or, how GenAI may facilitate plagiarism, that is, how copyrighted material is being used to train AI models without permission. So,  how to assess a GenAI-assisted process in project reviews when portfolios appear genuine and authentic? And so, from a pedagogical perspective, what exactly are students learning when using GenAI? And how do tutors assess accurately student competency versus tool competency? That is, to assess the learner's procedural and conditional knowledge rather than declarative knowledge or technical fluency, including problem-solving skills, material knowledge, and personal judgement? Moreover, what proof that students not only can prompt effectively, curate, edit, and direct outputs, but also use GenAI critically while understanding its limitations? GenAI-immersed design education, then, highlights new relationships between student, tool, tutor and faculty. The dynamic has changed: where does knowledge and authority reside, and what is being evaluated and valued?

Designing with GenAI

Designers commonly ask, what is GenAI good for? An automatic AI-generated summary by Google describes GenAI in positive terms as a co-creator that inspires thinking, facilitates rapid prototyping and reduces repetitive drafting and production tasks. But, negatively, GenAI is said to struggle with precision, original nuance, and ethical boundaries. In short, the risks are: 1. GenAI relies on pattern matching and cannot replicate genuine human taste, empathy, or deep emotional context. 2. Over-reliance on GenAI outputs can lower critical thinking and lead to generic design choices. 3. GenAI raises ethical concerns such as copyright, data bias,and transparency which remain major challenges across the design industry. However, intense political and cultural debates over bias, copyright and misinformation may overshadow genuine benefits of GenAI in creative fields. Yet GenAI is no substitute for originality and novelty and so human judgement and oversight remain essential for final outcomes.

Human creativity vs artificial intelligence

Human creative intelligence, rather than artificial intelligence seems a better descriptor of design ideation. This is because calling ideation artificial intelligence suggests design is akin to a math problem, and math is the underlying language of machine learning, LLMs, instead of a human creativity approach to problem solving. And so, rather than relying on data processing and pattern recognition, ideators should trust their intuition, critical thinking skills and lived experience. Indeed, the word "artificial" suggests something synthetic that lacks the depth of natural human thought. Yet the complexity of designing for today's fast moving competitive markets, and human intuition and trial-and-error are not sufficient. And so, human creative intelligence incorporates a wide range of conceptual tools, both analogue and digital, including AI. But whatever tools employed, designers should have oversight and retain final responsibility for all AI decisions and outputs, particularly as AI models are becoming more powerful going beyond prompt engineering.

Artificial ideas

Imagine all the world's recorded innovations, past and present, harvested by the tech giants, such as Google, Meta and OpenAI, and made accessible to the users of GenAI through prompting. This may not be that far-fetched as these companies not only use web crawlers to pull texts and images from the internet but also buy old archives and rare books from publishers and antiquarian booksellers. GenAI output, then is artificial creation ("content") by a machine ("productivity tool") and trained on massive data sets ("material"). But what impact does easy access to AI algorithm solutions have on developing human problem-solving skills? While GenAI stands for real technological progress, overly dependence on GenAI might reduce users' cognitive effort to think independently and critically in finding ethical and sustainable solutions for the many complex problems facing humanity. However, the impact of rapid technological progress, such as AI is often accompanied by human stress and worries, as witnessed throughout the modern period, from the industrial revolution onward. This suggests adaptation and human oversight are keys to managing tech anxiety.

Golden age of ideas?

In the light of the global reach and universal application of AI, and to paraphrase Winston Churchill as he was paying tribute to the Royal Air Force establishing air superiority over England in WWII, one might quip: "Never in the field of ideas was so much owed by so many to so few". The many, here, the users of GenAI, and the few the hard- and software pioneers and providers of AI models. Indeed GenAI is capable of producing instant ideas with a few prompts widening participation in the design process towards democratising design itself. And so, GenAI has become the ubiquitous ideation tool leaving users to "edit" or "curate" GenAI's results to their taste. That is, the users have become as much editors and curators of GenAI outputs as originators of their own ideas. But what is then an original idea? The French philosopher Baudrillard (1929-2007) argued that searching for the "true original" in the age of mass media becomes pointless. Copy, then, becomes the new normal, and, one may add, even more so under the influence of social media and GenAI usage. Yet GenAI has its weaknesses, such as the risk of superficial outputs or lack of real innovation. However, the format of digital copy and the endless recycling of imagery may, paradoxically increase the desire for seeing and experiencing the real thing as evidenced in young people's interest in analogue, tactile processes, such as analogue film, or vintage items, such as vinyl records.

Designing in the age of AI

AI is widening participation in the design process making design more populist in both scope and manner: "Everyone is a designer". But research suggests AI tools are most benefitting designers who already are high performers. That is, designers who know how to guide AI, how to integrate it with everyday design tasks as well as how to judge its output. Yet extensive use of AI tools, notably by entry-level or early career designers may underplay first hand observation and drawing skills as well as critical thinking and judgement. Or, why struggle with a problem when a chatbot can provide a neat, packaged response that might be regarded as "sufficient," or "good enough"? What then is the impact of AI on cognitive effort in matters of design? Could it be the case that in accelerating AI technology, "Man shapes AI and then AI shapes man"? That is, rather than based on human agency, AI models develop their own agency. That is, AI agents can also make decisions and take action to achieve a goal without human prompted. What, then is design and what makes a designer in the age of AI? If, say, human agency is finite, then what it means to be a designer is to grasp this finitude of designing, or "becoming one who is designing".

Prompting is creative communication

The creative abilities of GenAI models are artificial, not genuinely human. However, with human assistance, GenAI can produce creative content. But, as shown in experiments, GenAI (DALL-E3) output, which are clear and measurable, depend on user prompts (written instructions) as much as models*. This highlights how prompting is about communication which in turn reflects how verbalisation (words) is the most common means of human communication. This in turn suggests prompting is a creative process as well as a skill - a skill that improve with practice. In short, GenAI models need human intervention to produce independent creative output. *https://mitsloan.mit.edu/ideas-made-to-matter/study-generative-ai-results-depend-user-prompts-much-models