Data-driven ideas

In the early days of design research - 1960s, a scientific approach to to the design process was promoted as the result of new technical developments such as computers and automation. Although the design science relationship was, and remains controversial, scientific knowledge, and its application is still relevant to understanding how designers think and work (Nigel Cross, 2023). A recent example is GenAI models trained on terabytes of web crawl data from across the internet to create new content. Being data-driven means that GenAI's output is derived from data analysis, which is part of the scientific method. In the data-driven approach, GenAI represents a break from the more traditional view of design as a creative practice supported by artistic, intuitive processes or personal opinions. Yet GenAI does not exclude the designer from the ideation process because GenAI operates on a probabilistic framework, it does not possess true understanding or consciousness like a human. That is, GenAI is not capable of learning, or understanding the concepts underlying its own responses to human prompts, which are input elements for GenAI to generate results. Indeed to achieve human-centered desired outcomes, writing effective prompts is considered a skillful craft. And so, although GenAI evolved from computer science, the technology is guided by human prompts. GenAI, then, reflects human-machine interaction -  a tool that facilitates ideation.

Easy Aideas

Design ideas typically are about incremental change or improvement over something that already exists rather than about something that is truly original or radically different. For incremental innovation, then, generative artificial intelligence has emerged as a powerful ideation tool that facilitates a seemingly endless flow of creative content. But how does GenAI tools like ChatGPT and Copilot compare with human ideation without GenAI? GenAI foremost advantage is it generates huge amount of diverse content - both text and images using pre-existing data from across the internet. Then there is the ease of use of GenAI as it responds to written prompts in a conversational style and in multiple languages - and at great speed. More, as GenAI is going mainstream it has the potential of facilitating problem solving on a global scale. But there are weaknesses with GenAI models.. For example, writing effective prompts may seem simple but rests on prerequisite knowledge, language skills and, yes human imagination. Even so, the user friendliness of GenAI and the creative and original appearance of its output carry the risk of over-reliance on GenAI. Indeed users might find themselves having more ideas than they know what to do with. Also, it is not transparent what data GenAI models are trained on - raising ethical or copyright issues. Or, the self-referential loop of GenAI data might, paradoxically result in more similar output over time narrowing the scope for plurality or novelty. But values, assumptions and biases are embedded in GenAI tools, and so more empirical evidence is needed to fully evaluate the pros and cons of GenAI systems. Yet the appeal of GenAI is overwhelming and by fusing AI-generated ideas with human judgement and refinement, it is fair to say that GenAI is enhancing human creativity, including ideation.

Ideator roles

Design ideation can be seen as generating, developing and communicating ideas, and so the corresponding roles of the ideator would be that of generator, developer and communicator of ideas. And one may add a fourth role, that is, that of critic, or rather inner critic. For the designer as ideator, these roles often role into one. That is, the designer generates the idea, from a great variety of (re)sources, then develops the idea, that is, grow, evolve or expand the idea, and finally communicates, or shares the idea with others.The fourth role, that of inner critic, may run throughout the ideation process, a balancing role as the ideator should manage their self-criticism so as not harm the desire to succeed. However, there is potentially a fifth role for the ideator, that is, the role of realising, or implementing the idea to what then becomes a working idea. And to execute the idea would be either on the ideator's own accord or with the help of others. The designer-maker would exemplify the ideator playing all the roles whereas the "Jack-of all ideas" may typify the ideator as foremost generator and communicator of ideas.

Daydreaming

Daydreaming or mind-wandering is often seen as a spontaneous, unfocused mental state without deliberate direction - in contrast to imagination regarded as a more focused, structured, and purposeful cognitive process. And so, if you get stuck on a particular problem, say, it may be a good thing to take a break from the problem and allow the mind to wander and daydream for a time, to let your subconscious work the problem. Moreover, by stepping away from the task in front of you, chances are you may generate creative ideas that help solve the problem at hand. Although daydreaming may at first be seen as distraction from the present, that is, inattention - and daydreaming does not necessarily lead to creative manifestations - it may nevertheless help problem-solving as it frees up space for the mind to rest and wander. Allowing daydreaming then, can be intentional, also known as "positive constructive daydreaming". Deliberate daydreaming may sound counter-intuitive but in practice may take the form of finding space and time for sketching or doodling to allow the mind to roam or wander. Doodling then becomes a technique, a tool, or practice that supports daydreaming, or out-of-the box, creative thinking. In addition, daydreaming through doodling may give a sense of professional identification with the work - particularly when generative AI may reduce the capacity to daydream.

Participatory ideation

Digitisation of design (converting analogue data into digital format), or digitalisation (turning analogue processes into digital ones) are not only driving mundane or routine aspects of the design process (such as rendering, drawing a plan or elevation) but also influencing design thinking and ideation. And so designers are being challenged by algorithms and corresponding software structures to focus their efforts on things that humans are better at than machines. Or rather, when challenged, how designers who embrace AI may do better than those who do not.  That is, designers need to engage in the evolving computer-human mind relationship. For example, AI is moving in the direction of becoming closer to a super-competent assistant, a virtual team member or co-pilot in the design process. That is, using AI tools in an enhancing role. Moreover, digitisation of visualisation has transformed the way designers communicate with non-expert clients shifting the balance of power in favour of the client - clients who, through their own digital experiences are better equipped to engage in the design process and consequently more demanding in their design requirements. This development highlights how the designer-client relationship is an ever closer cooperation, collaboration or partnership. In other words, digitisation is enabling a multi-level participatory design process where the client and other stakeholders are effectively becoming co-designers.

Performative ideas

Design ideation is a purposeful activity with a performative function in that the idea amounts to a proposal, plan or promise to be acted on. Moreover, the performative function of ideation relates to the semantics of ideas. That is, semantics is concerned with linguistic meaning that takes the idea beyond the visual expression of the material content (material culture). Verbal language, then, can enhance the meaning of the idea and help explain why some ideas are, or at least appear better than others. The reason for this is that ideas are sometimes just informed or reasoned guesses that designers present to their target audience, say a client. Designers, then, move about and develop the idea till they are satisfied that the idea would meet  the expectation of the client brief. In this pursuit, generative AI, as ideation tool and learning model, has a performative function and a performative capacity too. That is, AI offers support to designers to improve their performance in generating, developing and communicating ideas.

An idea is an idea is an idea

Some design ideas have a direct relationship to an actual object where ideation is pragmatically pursued through the interaction with the physical environment. But there are also designer who seek the highest level of abstraction, or the purity of a concept where, for example, a building is conceived as an abstraction. Yet both pragmatic and rational attitudes and approaches to ideation are expressed in language; verbal, visual or abstract (numeric). But language is embedded in culture and represents meanings. For example, the concept of a house may differ from one culture to another, and from one era to another. Is there then a common language for expressing ideas that bridges such differences? If not, how is communication between designers and stakeholders possible? And how is change possible, for example, in designing homes, if change of the concept of home isn't changed, altered or modified, say, through working from home or multi-generational living? And so, with ideation begins responsibility because the successful idea must meet stakeholders' values and expectations.