Workplace Everyday-Creativity through aHighly-Conversational UI to Large Language Models
MICHAEL MULLER, IBM Research AI, USA
JESSICA HE, IBM Research AI, USA
JUSTIN D. WEISZ, IBM Research AI, USA
We explore everyday co-creativity for collaborative human-AI teams in workplaces via a conversational userinterface to a large language model. Previous short papers explored human-AI team-creativity methods suchas framing and reframing. This experiment examines aspects of brainstorming. We demonstrate divergent
thinking (idea generation) and convergent thinking (summarization and classification) between a human anda conversatioanl AI agent. We observed that creativity emerges in the hybrid interstitial conversational space
between human and AI.
CCS Concepts: • Human-centered computing → Empirical studies in HCI; Empirical studies incollaborative and social computing; Collaborative interaction; • Computing methodologies →Artificial intelligence.
Additional Key Words and Phrases: Human-centered AI, Co-creativity, Generative AI, Large language model,
Conversational UI
ACM Reference Format:
Michael Muller, Jessica He, and Justin D. Weisz. 2024. Workplace Everyday-Creativity through a HighlyConversational UI to Large Language Models. J. ACM 37, 4, Article 111 (August 2024), 5 pages. https://doi.org/XXXXXXX.XXXXXXX

1INTRODUCTION

We describe a recent case study in human-AI co-creativity, through team-creativity methods
between a human and a conversational large language model (LLM). We are interested in “everyday”
creativity, such as answering questions and creating solutions to problems, that knowledge workers
face many times each day. Creativity studies have used expressions such as “small-c creativity” [15],“mundane creativity” [5], or “P-creativity” [1] to describe these quotidian instances. Consistent withGlăveanu’s proposal of distributed creativity and Kantosalo’s and Takala’s conception of creativity
as performed by a collective of human and AI [11], we explore human-AI collaborations in everyday
team-creativity work. We hope to understand possibilities for human-AI co-creativity in ordinary
workplace collaborations.
To begin our exploration, we examined team-creativity practices [21, 30], and we chose brainstorming as familiar method that is used in many institutions. Earlier work examined methods offraming [18] and reframing [19] in analogy-based design. For our experiment, we chose a morestructured form of brainstorming (e.g., [9]) as a task that most teams perform on a regular basis.
Authors’ addresses: Michael Muller, michael_muller@us.ibm.com, IBM Research AI, Cambridge, MA, USA; Jessica He,jessicahe@ibm.com, IBM Research AI, Seattle, WA, USA; Justin D. Weisz, jweisz@us.ibm.com, IBM Research AI, Yorktown
Heights, NY, USA.
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Common stages in brainstorming include divergent thinking (idea generation) and convergent
thinking (idea summarization and categorization) [17]. These phases often correspond to higherlevel goals of exploration and optimization, as have been discussed in AI [22, 28], HCAI [7, 20],
HCAI-oriented design [29], CHI [12, 31] as well as influential critiques of AI planning approaches[27].
2BRAINSTORMING WITH THE AI

In this study, we used a highly-conversational user interface (UI) to the Llama2 large language
model (LLM), similar to [23] Each conversational turn by the AI was “tuned” to be brief, humble,
and helpful, through prompt design and some intermediate software between the UI and the LLM.
Conversations with the AI took place in an internal group chat application on a company-internal
server.
Figure 1 shows excerpts from an informal, one-user dialog during the divergent thinking phaseof the brainstorming activity. The human proposes a business problem as the topic of brainstormingat Figure 1A. The AI proposes “a few brainstorming ideas” at Figure 1B. While these ideas may not
be brilliant, we believe they are plausible and potentially useful.
Good brainstorming partners should be able to critique ideas and collect new suggestions [3].
Figure 1C-E shows a series of such interactions. The human objects to the fourth idea that the AI
had proposed. The AI offers alternative ideas that the human “could consider” (Figure 1D), but
insists that its original idea continues to have merit (Figure 1E). The conversation continues on
the right side of Figure 1F-G, with a second human objection, and a second set of AI-proposedalternatives. Ultimately, the human rejects all of those alternatives, and the problematic item aswell at Figure 1H.
Brainstorming can also involve a sudden expansion of the scope of a topic or a question. InFigure 1J, the human asks the AI a more open-ended question, and the AI responds at Figure 1K.
We also note that the work of the human and the AI moved between exploratory activities (divergent thinking, Figure 1A-B, D, G, J-K) and optimization activities (critical and convergent thinking,Figure 1C, F, H). These fluid changes in focus – within the broader context of brainstorming – appear
to be related to Schön’s analyses of designers’ reflective practice [24] as constantly developing
and emerging through conversations with the design materials [25]. If designer collectives (i.e., ofhuman and AI [11]) can move from divergent to convergent and back again – from exploration tooptimization and back again – then these observations call into question the binary distinctions
that have been discussed between exploration and optimization [7, 12, 20, 22, 28, 29, 31]. Further
study may show that exploration and optimization are in some cases simple alternations in focus
within a single holistic activity.
Figure 2 shows additional convergent thinking operations, in which the human has requested
a summary and categorization of the accepted ideas at Figure 2A, and then requests that the AI
move an idea from one category to another at Figure 2C. The AI first performs a critical thinking
task at Figure 2B, and performs more of a secretarial task, responding to the human’s instruction atFigure 2D.
3

DISCUSSION: THE LOCUS OF CREATIVITY

Unlike projects that elicit creative outputs from generative AI models [2, 16], we pursue the conceptof co-creativity as a process that is distributed [5] between the two parties of a human-AI collective[11]. We propose that creativity emerges through interaction, in the hybrid space between humanand AI (see e.g., [14]). For example, the human framed the problem space in Figure 1A, and the AI
proposed ideas into that frame in Figure 1B. Within a more critical subframe, the pattern repeatedin Figure 1C-D and again in Figure 1F-G. An even stronger example is the exchange that begins
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Fig. 1. Divergent thinking between human and AI. Yellow-highlighted text shows AI’s humble (rather thanoracular) conversational moves, including responsiveness to human correction. Blue-highlgithed text showsdivergent thinking requests. Grey-highlighted text shows critical and convergent thinking. Green-highlightedtext shows persistence of one idea. Teal-colored text shows a request for “blue-sky” speculation, answered bymustard-highlighted text. A. Human begins the brainstorming with a request for divergent thinking. B. AI
provides divergent ideas. C. Human criticizes one idea and requests alternative. D. AI proides alternatives. E.
AI presists with one idea. F. Human makes a second request for alternatives. G. AI provides alternatives. H.
Human rejects an idea. I. AI complies. J. Human requests “blue-sky” speculation. K. AI complies.

in Figure 1D with the AI’s suggestion for peer-to-peer support, which the human picks up again
at Figure 1I. The human asks the AI to pursue the topic of Figure 1D, and the AI responds with afurther series of novel suggestions.
4CONCLUSION

Glăveanu proposed that creativity could be distributed among persons [5], as a more focused versionof the concept of distributed cognition [8]. While their emphasis was on the social distribution ofcognition and creativity, researchers also showed how objects and record-keeping systems werealso involved in the distribution of knowledge [6], and in some cases of action [13]. Scholars suchas Jordanous, Kantosalo, and Takala extended this line of thinking into the partial equivalence ofhuman and AI in creativity ([10]; see also [4, 26]) and the necessary co-participation of humanand AI in co-creativity [11]. In this paper, we have applied that theorizing to the practical case ofhuman-AI brainstorming, where we have demonstrated that creativity emerges not from one partyor the other, but rather through the interaction of diverse human and AI actions.
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Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009

J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2024.