When ( echo ) chambers of creativity happen, ateliers of divergence get smaller yet is this an Ai issue ? Or maybe this is a time to ignore there is a room all together issue? Absolutely.
Ai ( and especially LLM's + generative based system designs associated with higher dimensional gradient descent neuronal architectures ) and even when configurations such as Feedforward ; Perceptron + Multilayer Perceptron ; Radial Basis Function ; Recurrent ; and Modular back propagation system designs are examined, we many examples of configurations localizing on the same situation in the end: pushing the true stimulation of divergent thinking, concept blending, and creative honing theory together plus design solution sets which are all bound squarely in the realm of constraint based training due to design, engineering and financial requirements and which are the bounds we have to deal with when delving deep into Ai effectiveness.
In this " edge " & limitation based typological thought exercise Ai systems are able to combine known experience and accepted creativity output to expand possible design solutions yet where " edge " parameters are the exact limitations that these Ai systems face. Edge garbage in is still edge garbage out. But then what ?
In Effects Of Design Thinking On Artificial Intelligence Learning And Creativity we see the capability of Ai systems ( as a whole and where generative Ai takes center stage ) to directly effect design thinking outcomes which have significant effects on relational classes of AI concepts & output: design thinking in the end drives edge boundaries that Ai systems employ to bring further and more extreme solution sets into being. Ai systems designed to push edge boundaries are in the end only reinforcing said boundaries.
We then see how Design thinking mentalities and their models often a set of divergent investigation can positively affected learning attitudes toward AI: this is where Ai often shines the brightest, providing quick and dirty investigations into alternative solution sets in very short time and especially in the visual realm. Where it would have taken days or weeks to go from conceptual directions to final " possible " visual and even functional prototyping, this now takes seconds with the aid of Ai systems but this does not mean a further pushing of the edge boundaries, only the center mass. More importantly we see how from many research sources and in-depth user-centered thinking and design thinking systems that thematic, project-based, and daily life contextual design thinking can be a way to further provide instructional design data sets for Ai and ML learning & training but again this is moving toward central mass mentalities and again, pointing inward based on edge constraints.
However what is particularly interesting is how design thinking's effect on the perceived creativity of works associated with Ai and further elaboration where novelty of ideas and product creativity as connected to ideation models such as many of the currently accepted systems like the SCAMPER (substitute, combine, adapt, modify, put to another use, eliminate, reverse) technique ; brainstorming ; the Six Thinking Hats model; and attribute listing clearly allow for ( in the ideation stage and when combined with " learning " AI systems ) we see improved creative expression but to what extent ? Where this may seem tautological ( generative Ai systems literally are creative and interpolative engines ) what is not easily accepted is how Ai generative capabilities allow for truly unexpected ways of arriving at alternative strategies and thus output that can and does define valuable and defendable avenues for new product development efforts. The further afield that ideas come from the more alternative and valuable NPD efforts become if and only if these possibilities are not rooted too far from existing norms and their respective system implications.
Where novelity and expanding creativity efforts for enhanced solution set delivery are important factors in the development of new and unexpected directions for possible innovation activities and NPD goals with Ai, the question of is Ai ( generative, LLM based and other ) a true driver for alternative and thus important divergent directions vs. just amplifying the edges of exiting design, engineering and finance topological constraints ? In many cases the reality is that it is doing both at the same time but where the norm is to say that Ai systems are bound themselves by the notion of how much and how " alternative " they can be expected to evaluate and act upon particularly when direct contradictions to outcomes occur in such " both " scenarios and where powerful input by those driving such creative and tactical efforts come to bear.
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