2407282337 implications of prolonged use of generative AI tools in professional development
2407282337 implications of prolonged use of generative AI tools in professional development
futures of architectural thought
Two papers popping up in the search results has corroborated in part my observations from our collective time of 24ish months with generative AI. I add extra time of Dalle2 experiments here. The norm is to use superlatives to describe machined generations since it looks high quality to an untrained individual. After social media knowledge is exchanged more to entertain rather than inform. This shift, especially in expert agnostic disciplines such as the arts has replaced formal discussions, thereby disrupting the information networks which gives knowledge form. Machined material feeds is now the new wave to add to the already disrupted landscape.
We are in the novelty phase of the technology therefore all research and its subsequent findings are limited to two or at most three years of data gathered within distinct controlled environments. Findings therefore will change, it is therefore worth keeping track of how generative AI is impacting our imagination of the world through material access on it. The premise both papers present is there is collective creativity at the novice level but for an expert there is no significant change in their output. For those who fall in the category of experts their pursuit of innovation drops when there is no significant returns against their machined generated output vs original work.
Previously the designated expert builds their work as reaction or an extended of other experts in the field now time is lost in rectifying, low quality, often times similarly patterned output. The traditional training which has relied on critiquing and finding gaps, in knowledge contributed, with generative AI a new type of material now is available to build from. This relearning can slow down the making new work that has developed a systematic workflow. Getting used to the tone of machined outputs and integrating it within an already finetuned habit can result in lost time which should ideally have been directed elsewhere.
It is the novice who has gotten better but the expert has lost out. What is disciplinary expertise after generative AI is the gap in the two papers to build on. Everyone now makes work at the same level for e.g., how do you evaluate the difference between a generated image by a student against that of a professional? My hypothesis to test here is the future of architectural expertise are those with designed workflows. Processes will guide making and therefore defining who attains expertise in their efficient implementation that it creates new domain knowledge. The knowledge product may have lost its wonder but processes that moves the discourse forward will be recognised.
References & Links
The Creativity Code by Marcus du Sautoy, Open Letters Review
The Double-Edged Roles of Generative AI in the Creative Process: Experiments on Design Work
Generative Artificial Intelligence (GenAI) mimics human creativity by producing novel and complex content solutions across various creative domains, making it distinct from AI in prior studies. Our research is among the first to not only establish the overall impact of GenAI on creative work, but also delve into GenAI’s impact on the creative process, addressing a significant gap in the existing literature that largely views creativity as an end product rather than a cognitive process. Drawing on theories of creativity and cognitive fixation, we conceptualize creativity as a process that encompasses an initial ideation stage and a subsequent implementation stage, and we theorize the differing impacts of GenAI on these two stages, depending on the expertise level of the human creators. Findings from our field experiment (involving a real-world product design task) show that GenAI significantly improves the overall quality of creative work, especially among human creators with less expertise. Our lab experiment (involving a graphic design task) further demonstrates that GenAI tremendously boosts creative work by facilitating divergent thinking in the ideation stage. In the subsequent implementation stage, GenAI still benefits creators with less expertise. However, for high-expertise creators, GenAI considerably reduces their work efficiency without improving their design quality, suggesting that GenAI may disrupt the established work approaches of high-expertise creators to converge on a final design. Our work contributes to the burgeoning GenAI literature by elucidating the nuanced impact mechanism of GenAI in the creative process and the heterogeneity of such impact based on creators’ expertise levels. Our work also highlights the importance of recognizing the double-edged impacts of GenAI on creative work, as well as the need for its careful integration.
Generative artificial intelligence enhances creativity but reduces the diversity of novel content
Creativity is core to being human. Generative artificial intelligence (GenAI) holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on GenAI ideas. We study the causal impact of GenAI on the production of a creative output in an online experimental study where some writers are could obtain ideas for a story from a GenAI platform. Access to GenAI ideas causes an increase in the writer's creativity with stories being evaluated as better written and more enjoyable, especially among less creative writers. However, GenAI-enabled stories are more similar to each other than stories by humans alone. Our results have implications for researchers, policy-makers and practitioners interested in bolstering creativity, but point to potential downstream consequences from over-reliance.
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