Design Education

For years, platforms like YouTube and Instagram have served as vital conduits for delivering design learning resources and skill demonstrations. This social media ecosystem allows inquisitive individuals to find free content to up-skill or support their existing expertise.

Indeed, the sheer volume of design blogs, demonstration videos, student websites, consultancy case studies, and portfolios means that the constituent components of a variety of different design courses already exist online.

However, while finding materials to inform a specific, isolated skill is easy, capturing an entire subject remains difficult; the necessary resources are scattered and diffused across the internet. Consequently, this reliance on free resources is rarely used for anything more than supplementation.

Until now, walled-in educational institutions have not been threatened by this decentralised content, retaining the crucial advantage of collated materials held in one place as an expert curation of knowledge.

This could all change with Large Language Models and AI. LLMs will be able to surface this diffused information and go much further.

AI-driven platforms could provide students with high-quality tutoring content that is unique to the individual, by tailoring a vast spectrum of personal criteria.

It could map learning pathways for the type of design the student wishes to specialise in, covering the spectrum from conceptual furniture to design engineering. Content that instructs on design processes, output types, theoretical frameworks, and practical skills like ergonomics or manufacturing constraints would be customised to the speciality.

Furthermore, AI can package these creative materials in the exact formats a student prefers, whether podcasts, notes, or videos. Organised in the correct order and cadence of their learning, to match individual learning styles, the "syllabus" will no longer be the exclusive property of universities or educational institutions, but an adaptable resource accessible to anyone with a connection to the web.

Because the next generation of designers are digital and AI natives, we can expect a natural, seamless adoption of these tools.

Future students will be able to oscillate between generative ideation and traditional craft, viewing AI simultaneously as a tutor, a peer, a tool, and, in some cases, the product itself.

This has massive institutional implications. Brett Akop believes AI could democratise design education, meaning that aspiring designers won't need to go to the "right" expensive schools.

Having been schooled in the USA, Akop is highly familiar with the geographic and financial gatekeeping that characterises traditional design education. By removing the need for massive physical infrastructure and reducing the cost of expert-level guidance, AI could allow unprecedented access for talent from previously under-resourced communities.

However, while AI has the potential to level out educational access, it could also have a distinctly negative impact on the process of learning and a student's ability to retain knowledge. Bruno Schillinger expressed concern that AI will effectively remove the need to learn because it will always get you "far enough."

If we no longer need to go through the rigorous learning process, we won't necessarily remember any of it, fundamentally changing what it means to learn. This shift introduces a "cognitive paradox": while AI enhances procedural skills, it simultaneously erodes conceptual depth.

Bruno believes it is vital to live with an idea, and perhaps even to struggle with it for a while to get the best results. When a machine can instantly generate a perfectly rendered product sketch or an optimised mechanical assembly, the student risks bypassing the productive struggle necessary for deep memory and a critical understanding of the process.

As curriculum content and learning itself take on new forms, design education institutions must urgently reassess where value lies for future students, moving beyond the delivery of information or practical demonstrations.

Given that AI has the potential to reduce the number of existing design jobs in the market, part of the university's modern mandate must be to help students find their place in a shifting world, by exploring the landscape for new opportunities and niches that AI won't be able to fill.

While the knee-jerk reaction is to define these niches as purely "more human" or more emotional, there is a distinct possibility that design students will also crack open entirely new areas of potential, using AI. They may discover new types of jobs, design new kinds of industry services, and invent completely new categories of products.

Tutors' knowledge of the commercial creative space will be invaluable to identifying such opportunities. It's possible, however, that students might know more about how to use AI than their professors will. This may challenge the traditional hierarchies of student and teacher, so perhaps explorations of AI and design will look more like collaborations, than the top-down learning of old.

To Bruno's point, a reassessment of "knowledge" may also be needed to ascertain what and how much a student actually needs to "learn" in the context of this new landscape. The internet and also current design tools supplement human memory — and AI will continue this.

In the face of processes made simpler and easier by AI, knowledge and skills must be engaging and useful enough to be worthy of committing to memory. Furthermore, institutions must ensure students think critically, avoid getting stuck in echo chambers, and learn how to push the boundaries of these new technologies.

Education institutions have an incredible and vast opportunity to redefine both what it means to learn and what it means to design. They must embark on their own productive struggle or risk becoming irrelevant.