From studio practice to cognitive partnership: How AI is changing design education

The rapid adoption of generative AI in universities has raised urgent questions about learning integrity, authorship, and the future of creative education. In design programs, where process visibility and reflective practice are crucial, AI-generated outputs are challenging educators to define new boundaries between assistance and authorship.

Those challenges are addressed in From Tools to Thinking Partners: Cognitive and Pedagogical Shifts in Design Education Through Generative AI, published in Arts and Humanities in Higher Education, which documents how design educators are responding to generative AI across different cultural and institutional contexts.

From digital tools to cognitive partners in the design studio

Design education has long adapted to technological change, from the rise of desktop publishing to the expansion of interactive media and user experience design. In earlier transitions, new technologies primarily altered how designs were produced rather than how ideas were formed. Generative AI represents a break from this pattern. Unlike previous digital tools, generative AI intervenes directly in ideation and evaluation, two core cognitive processes traditionally central to design learning.

GenAI systems can now generate concepts, variations, and explanatory text in seconds, effectively externalizing parts of creative thinking that were once internal and iterative. This shift moves design education toward a model of human–AI collaboration, where students interact with algorithms that propose ideas rather than merely execute instructions. The study calls this a redistribution of cognitive labor, with AI acting as an external system that supports, challenges, and reshapes human reasoning.

The research is based on qualitative interviews with nine academics teaching design-related disciplines. Educators consistently describe generative AI as more than a productivity aid. Across all contexts, it is understood as something that influences how students frame problems, articulate intent, and judge outcomes. In well-resourced environments such as Denmark, generative AI is increasingly integrated as a structured component of studio pedagogy, aligned with professional expectations and industry practices. In these settings, AI is positioned as a thinking partner that students are expected to guide deliberately rather than use passively.

On the other hand, educators in Indonesia and El Salvador operate under tighter resource constraints and more deeply rooted manual traditions. Here, generative AI is often introduced cautiously and selectively. Rather than embedding AI across all stages of studio work, educators emphasize foundational skills such as drawing, collage, and material exploration before allowing students to engage with algorithmic generation. This approach reflects a concern that premature reliance on AI could short-circuit cognitive struggle, which is seen as essential to creative development.

Despite these differences, the study finds a shared recognition that generative AI challenges long-standing assumptions about creativity as a purely human endeavor. Design educators are no longer simply teaching students how to use tools but are renegotiating the boundaries between human intention and machine-generated possibility.

Manual-first pedagogy and the shift from making to curating

Most importantly, the study finds the persistence of manual-first pedagogy in less digitally saturated contexts. In Indonesia and El Salvador, educators consistently describe hands-on making as a cognitive anchor that develops sensitivity, judgment, and reflective depth. Manual practices are not treated as outdated skills to be replaced by automation but as essential foundations that prepare students to engage critically with AI-generated material.

This emphasis reflects a broader concern that generative AI, by producing immediate results, risks bypassing the slow, embodied processes through which students learn to see, feel, and evaluate design quality. When AI generates hundreds of options instantly, the traditional rhythm of sketching, iteration, and critique is disrupted. The study shows that educators in manual-first contexts deliberately resist this acceleration by requiring students to demonstrate conceptual understanding and craftsmanship before engaging with generative systems.

At the same time, the research documents a clear shift in the designer's role across all regions studied. As AI systems increasingly handle exploratory ideation, students are moving from being primary makers to becoming directors or curators of AI-generated outcomes. This role change places greater emphasis on conceptual clarity, decision-making, and critical evaluation. Students are expected to guide AI systems with intent, assess the relevance and originality of outputs, and justify why certain results are accepted or rejected.

This transformation redefines creativity as a process of selection and judgment rather than solely production. It also introduces new pedagogical challenges. Educators must now teach students how to articulate design intent precisely and how to reflect on the implications of delegating ideation to algorithms. The study highlights prompt literacy as a major emerging competency. Prompt literacy refers to the ability to communicate ideas effectively through language in order to shape AI behavior, making linguistic precision and conceptual thinking core design skills.

The findings suggest that this shift does not necessarily diminish creativity but changes where creative effort is concentrated. Instead of investing time primarily in producing artifacts, students invest cognitive effort in framing problems, evaluating options, and reflecting on authorship. However, the study cautions that without strong pedagogical guidance, this shift risks reducing originality and weakening students' connection to their work.

Ethics, inequality, and the global shape of AI-driven design education

The study also identifies ethics and access as defining issues in the integration of generative AI. Across all regions, design educators express concern about authorship, originality, and accountability in AI-assisted work. As AI systems draw on vast training datasets, questions arise about bias, appropriation, and the legitimacy of creative ownership.

The research shows that ethical awareness is not treated as an abstract concern but as a practical teaching responsibility. Educators increasingly require students to explain how and why AI was used in their design process, reinforcing transparency and reflection as part of assessment. This emphasis reflects a broader shift toward what the study describes as epistemic vigilance, the ability to question the reliability, provenance, and implications of AI-generated content.

Importantly, the study highlights how material conditions shape ethical and pedagogical responses to AI. In Indonesia and El Salvador, limited access to premium AI tools and institutional funding constrains adoption. However, rather than hindering innovation, these constraints often foster more reflective approaches. Educators in these contexts tend to integrate AI conceptually rather than procedurally, encouraging discussion about its role and limitations rather than focusing on production speed or visual polish.

Conversely, Danish institutions benefit from stronger infrastructure and closer alignment with industry expectations. This enables broader experimentation with generative AI but also creates pressure to prepare students for professional environments where AI use is increasingly normalized. Even in these settings, however, educators draw attention to documentation and justification to ensure that AI integration strengthens rather than undermines design standards..