# The Effects of Generative AI on Design Fixation and Divergent Thinking

Samangi Wadinambiarachchi  The University of MelbourneMelbourneAustralia3000
<EMAIL_ADDRESS>,  Ryan M. Kelly  RMIT
UniversityMelbourneAustralia3000<EMAIL_ADDRESS>,  Saumya Pareek  The
University of MelbourneMelbourneAustralia3000<EMAIL_ADDRESS>,
Qiushi Zhou  The University of MelbourneMelbourneAustralia3000
<EMAIL_ADDRESS>and  Eduardo Velloso  The University of
MelbourneMelbourneAustralia3000<EMAIL_ADDRESS>
(2024)

###### Abstract.

Generative AI systems have been heralded as tools for augmenting human
creativity and inspiring divergent thinking, though with little empirical
evidence for these claims. This paper explores the effects of exposure to AI-
generated images on measures of design fixation and divergent thinking in a
visual ideation task. Through a between-participants experiment (N=60), we
found that support from an AI image generator during ideation leads to higher
fixation on an initial example. Participants who used AI produced fewer ideas,
with less variety and lower originality compared to a baseline. Our
qualitative analysis suggests that the effectiveness of co-ideation with AI
rests on participants’ chosen approach to prompt creation and on the
strategies used by participants to generate ideas in response to the AI’s
suggestions. We discuss opportunities for designing generative AI systems for
ideation support and incorporating these AI tools into ideation workflows.

Design fixation, Generative-AI, Creativity support tools

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642919††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Empirical studies in HCI

##  1\. Introduction

Consider a team of designers discussing ideas for environmentally friendly
transport solutions for a city. One team member kicks off the discussion with
a suggestion about electric buses. The rest of the team then spends an hour
discussing variations on this idea, all involving electric vehicles, until an
intern who arrived late asks “have you considered bicycles?”. Until the
intern’s suggestion, the ideas were anchored on a salient characteristic of
the first proposal—an electric motor. The design literature dubs this
phenomenon design fixation—the “blind adherence to a set of ideas or concepts
limiting the output of conceptual design” (Jansson and Smith, 1991, p. 1).
This is a common experience in any creative task, from art to engineering, and
happens when exposure to one idea anchors and biases subsequent ideas,
restricting exploration of the design space. Fixation happens both consciously
and unconsciously, regardless of the level of experience of the practitioner
(Jansson and Smith, 1991; Youmans and Arciszewski, 2014b) and in all areas of
creative work. The severe negative impact that design fixation has on the
creative process makes it a key concern in design studies.

In the initial stages of the design process, it is common for designers to
conduct precedence studies and create mood boards by compiling external
stimuli as sources of inspiration to broaden their ideation space (Lucero,
2012). However, the exposure to previous solutions during this process can
potentially be a source of design fixation. Previous studies have shown that
exposure to examples of similar design solutions has mixed effects on
creativity (Youmans and Arciszewski, 2014a). It tends to drive designers
towards the example, narrowing the explored solution space (Jansson and Smith,
1991; Leahy et al., 2020). Further, variations in the modality (Viswanathan et
al., 2016; Vasconcelos and Crilly, 2016), the fidelity (Cheng et al., 2014;
Vasconcelos and Crilly, 2016), the quality (Vasconcelos and Crilly, 2016), the
diversity and novelty of the exposed stimuli, the time of exposure, and its
proximity to the design problem (Vasconcelos and Crilly, 2016) can vary the
intensity of design fixation (Vasconcelos et al., 2017).

Recent developments in generative artificial intelligence (GenAI) have been
heralded as the harbinger of a new paradigm of creative work, often under the
guise of augmenting human creativity (Eapen et al., 2023). Publicly available
AI image generators such as DALL·E111https://openai.com/dall-e-2,
Artbreeder222https://www.artbreeder.com, Stable
Diffusion333https://stablediffusionweb.com, and
Midjourney444https://www.midjourney.com have made it possible for designers to
turn their thoughts into high-quality visuals quickly and at a low cost. The
ability of these tools to generate “original” images based on user prompts
potentially offers a rich source of inspiration. For example, Chiou et al.
(Chiou et al., 2023) have shown that when used in co-ideation tasks, AI can
open up a broader conceptual space quickly and effortlessly, promoting
divergent thinking (Chiou et al., 2023). However, there is still a lack of
empirical evidence for the effect of generative AI as a source of inspiration
during design tasks. Though the specific outputs generated by these tools are
novel, they are trained on existing work, blurring the lines between what is
original and derivative. Further, designers could still be fixated during the
ideation process despite any potential inspiration from AI.

In this paper, we aim to understand the effects of AI-generated imagery as a
source of inspiration in an ideation task. We conducted a between-participants
experiment in which designers took part in a visual ideation task that
involved sketching ideas for a chatbot avatar. We manipulated participants’
access to sources of inspiration: none, access to Google Image Search, or
access to Midjourney (an image generation AI tool). Through our study, we
sought answers to the following questions:

  * •

RQ 1: How does the exposure to AI-generated images affect design fixation and
divergent thinking during ideation, compared to using commonly used sources of
inspiration and no inspiration support?

  * •

RQ 2: How do different ways of interacting with AI image generators impact
participants’ effectiveness in an ideation task?

We evaluated the effect of inspiration sources on participants’ ideation
output (the sketches). In doing so, we used four divergent thinking measures
from prior literature (design fixation score, fluency, variety, and
originality (Jansson and Smith, 1991; Shah et al., 2003; Youmans and
Arciszewski, 2014b; Vasconcelos et al., 2016)) to assess different facets of
their creative output. We found that exposure to AI-generated images induced
higher design fixation in participants than in other conditions. Moreover,
fluency, variety, and originality were lower in the AI-supported group
compared to the baseline condition. Through our qualitative analysis, we
suggest that fixation arises when creating prompts and when ideating in
response to AI images. In addition, we demonstrate that using AI can result in
fixation displacement, where the focus of fixation shifts from an exemplar
onto the AI’s outputs.

Our study provides an empirical contribution to the AI-powered creativity
support literature by illustrating how AI-generated images influence design
fixation and divergent thinking measures. It further elaborates on AI’s role
in providing inspiration during visual design tasks. Further, we demonstrate
the importance of focusing on factors that might induce design fixation while
acquiring inspiration from AI tools and propose potential strategies and
directions to explore in mitigating design fixation.

##  2\. Related Work

Our research builds on studies of design fixation and on the role AI can play
in supporting design ideation.

###  2.1. Design Fixation

Among the factors that hinder designers’ creativity, “design fixation” is one
of the most well-studied phenomena in creativity and design research. It is
identified as the unconscious adherence to a set of pre-known ideas or
knowledge that restricts the ideation space (Jansson and Smith, 1991; Youmans
and Arciszewski, 2014b). When a person experiences design fixation, they tend
to adhere to pre-conceived ideas and concepts, limiting exploration of the
design space during ideation (Linsey et al., 2010; Purcell and Gero, 1996;
Jansson and Smith, 1991). Design fixation narrows designers’ ability to
explore the creative space between abstract ideas and potential solutions
(Vasconcelos and Crilly, 2016; Jansson and Smith, 1991). Previous findings
show that this is reflected heavily in their design outcomes and restrains
designers from maximising their creative potential, resulting in unoriginal
outputs (Jansson and Smith, 1991).

Design fixation has been studied extensively across different fields
(Vasconcelos et al., 2018), including cognitive science (Cao et al., 2021),
design (Bellows et al., 2013; Kim et al., 2013), education (Howard et al.,
2013), mechanical engineering (Youmans, 2011b; Viswanathan and Linsey, 2012),
and psychology (Smith et al., 2013; Bellows et al., 2012). These studies have
collectively shown that design fixation is more likely to occur when designers
are exposed to example solutions for design tasks (Jansson and Smith, 1991).
It has also been demonstrated that the modality, degree of abstraction of the
inspiration (i.e. the fidelity), and the designer’s level of expertise
(Alipour et al., 2018) can affect fixation intensity when exposed to external
stimuli.

Fixation has typically been studied through quantitative experimental
approaches in which participants are asked to solve a design problem, either
with or without an example (external stimuli) (Vasconcelos and Crilly, 2016).
For instance, Jansson and Smith’s classic design fixation work (Jansson and
Smith, 1991) reported four experiments. These experiments divided participants
into two groups: a treatment group (fixation group), who were given a design
problem along with an example solution, and a control group, who were given
the same problem with no examples to work from. They hypothesised that showing
an example design would restrict the ideas of the treatment group because it
would make the participants fixate on the given example. Jansson and Smith
(Jansson and Smith, 1991) found that even though both groups produced a
similar number of designs, ideas in the fixation groups were more similar to
the example. In a subsequent experiment, the researchers found that the
flexibility and originality of the designs were limited in the fixation group
and concluded that creative performance may be inhibited when an example
induces design fixation. Since then, several studies have been conducted
replicating or amending the method and examples (Vasconcelos and Crilly, 2016;
Leahy et al., 2020).

When looking at design fixation, it is important to distinguish different
types of fixation effects (Crilly and Cardoso, 2017). Youmans and Arciszewski
(Youmans and Arciszewski, 2014b) identify three such effects. The first is
unconscious adherence (Youmans and Arciszewski, 2014b) to past designs without
realizing. An example of this is copying the features of an example (even if
the features are inappropriate to the task) (Jansson and Smith, 1991; Crilly
and Cardoso, 2017).The second is conscious blocking (Youmans and Arciszewski,
2014b), where new ideas are actively but perhaps momentarily dismissed. In
this situation, a designer is aware of alternative creative paths but chooses
to disregard them, perhaps due to a commitment to a current project’s
direction or a bias towards familiar solutions. The third type of fixation
effect is intentional resistance, a deliberate decision against exploring new
concepts. For instance, design companies engaged in research and development
often prefer to explore solutions that fall within their well-established
expertise, a tendency known as local search bias (Rosenkopf and Nerkar, 2001;
Crilly and Cardoso, 2017).

Apart from trying to understand its causes, researchers have explored various
strategies to overcome design fixation (Vasconcelos and Crilly, 2016). Such
strategies include incorporating physical prototyping in the ideation
activities (Viswanathan and Linsey, 2012), triggering frequent reminders for
participants to consider all available options in a timely manner during an
ideation task (Luchins, 1942; Youmans and Arciszewski, 2014b), utilising
design thinking and lateral thinking methods (Belski and Belski, 2015) such as
de Bono’s six thinking hats (de Bono, 2008; Andersson et al., 2012), having
short breaks or “incubation periods” during the task (Youmans, 2011a; Smith
and Linsey, 2011), using computer-aided design and intelligent agents (Davis
et al., 2019; Hoggenmueller et al., 2023), and incorporating design by analogy
(Casakin and Goldschmidt, 1999).

Even though there is a large body of work on design fixation exploring the
effects of external stimuli on creative tasks (Alipour et al., 2018), studies
centred around design fixation are limited within the field of HCI. Among
these few studies, HCI researchers have started to examine the potential of
using AI image generators as tools for supporting creativity (Lewis, 2023;
Hoggenmueller et al., 2023). Thus, in this study, we adapt experimental
methods from mechanical engineering and design research, where design fixation
is framed as unconscious adherence and is measured by the degree to which
participants directly copy features from an example stimulus. We aim to
understand the influence of AI image generators on design fixation and
divergent thinking, adding new empirical evidence to the HCI literature.

###  2.2. The Emergent Role of AI in Creativity Support

Since the early 1990s, designers have envisioned a future with intelligent
design and creative aids (Gero et al., 1994). With recent advances in
Generative AI, this vision is becoming a reality. Generative AI systems can
create new, plausible media (Remy et al., 2020) to aid individuals in creative
tasks (Joon et al., 2021). Generative AI models are trained on large data sets
and can enable people to generate content such as images, text, audio, or
video quickly and easily (Koch et al., 2020). Currently, Generative AI tools
enable users to create diverse artefacts by providing instructions in natural
language called “prompts”. Generative AI systems can also synthesise diverse
concepts and generate unpredictable ideas. In the case of AI image generators
— the focus of our study — the output comes from the latent space of a deep
learning model, arising from an iterated diffusion process that involves the
model arranging pixels into a composition that makes sense to humans
(Verheijden and Funk, 2023). Because of process randomness, different results
can be obtained based on the same prompt, with entirely new images each time.
This differs from conventional image searches, where the search is performed
by entering a query into a database to retrieve images that the search engine
considers relevant. Another difference is that whereas long and specific
queries might be too restrictive for an image search engine, they can benefit
AI image generators.

Previous works have explored the roles that generative AI can play in the
creative process (Hwang, 2022). For instance, AI can generate content entirely
by itself with instructions from the user, or it can act as a creativity
support tool, augmenting the user’s creativity (Mazzone and Elgammal, 2019).
AI text generators can be used as a tool to define specific problems to solve
and promote convergent and divergent thinking (Wingström et al., 2022) and
have the potential to be used as a co-creative assistant for a designer (Davis
et al., 2019; Shin et al., 2023). Professionals in creative industries claim
that AI could be a promising tool to gather inspiration (Audi MediaCenter,
2022).

With the growing interest in AI, HCI researchers have also started to explore
ways of using AI as a creativity support tool (Chung, 2022; Joon et al.,
2021). Among these explorations, a growing stream of literature focuses on
using generative AI to access inspiration and mitigate design fixation.
Researchers speculate that generative AI will become a potential solution for
inspiring designers (Lamiroy and Potier, 2022; Sbai et al., 2019; Singh et
al., 2019) due to the ability of AI generators to create abstract and diverse
stimuli (Karimi et al., 2020).

One of the early examples in HCI for incorporating AI to mitigate design
fixation was the Creative Sketching Partner (CSP) (Davis et al., 2019; Karimi
et al., 2020), an AI-based creative assistant that generates inspiration for
creative tasks. Through multiple studies, Davis et al. (Davis et al., 2019)
suggest that the CSP helped participants in ideation and in overcoming design
fixation. Hoggenmueller et al. have also explored how generative text-to-image
tools can support overcoming design fixation experienced in the field of
Human-Robot Interaction (Hoggenmueller et al., 2023). They conducted a first-
person design exploration and reflection using “CreativeAI Postcards” inspired
by Lupi and Posavec’s “Dear Data book” method to ideate and visualize robotic
artefacts. They noted that AI-generated images have the potential to inspire
new robot aesthetics and functionality and also claimed that the designer’s
AI-co-creativity can help to eliminate biases and expand limited imagination.
In a different case, Lewis (Lewis, 2023) reflects that a digital assistance
tool like “ChatGPT” helped her by acting as an art teacher and providing
instructions. Lewis points out that it is challenging to distinguish between
inspiration and copying when utilizing generative AI and reflects on concerns
such as “transparency of attribution”, “ethical considerations”, and the
clarity of the “creation process”. Rafner et al. (Rafner et al., 2023)
conducted an in-the-wild study to examine the effects of AI-assisted image
generation on creative problem-solving tasks, aiming to investigate the
effects of generative AI on problem identification and problem construction.
They developed a human-AI co-creative technology that combines a GAN and
stable diffusion model to support AI-assisted image generation. They found
that this intervention enabled participants to facilitate idea expansion and
prompt engineering, suggesting that AI can “aid users in generating new ideas
and refining their initial problem representations” (Rafner et al., 2023).

As the domain of AI-powered creativity support is still in its infancy, the
available literature provides only a nascent understanding of the effect of AI
on creativity and design fixation. Our work extends the literature by using
established techniques from design fixation research to better understand how
AI image generators affect design fixation during a visual design task.

##  3\. Method

We conducted a between-participants experiment to understand how AI-generated
imagery affects designers’ divergent thinking during visual ideation after
being exposed to an example design. We compared this scenario to the use of
online image search and to no inspiration support. The independent variable
was the Inspiration Stimulus: none (Baseline), Google Image Search (Image
search), or Generative AI (GenAI). The dependent variables were the Design
Fixation score (the number of features in each sketch in common with the
example), Fluency (the number of sketches produced), Variety (the number of
different types of sketches produced), and Originality (how infrequently other
participants devised the same type of sketch). We conducted the experiment in
a controlled laboratory setting following a mixed-method approach. All
participants gave informed written consent to participate after reading a
plain language statement describing the procedure. The study received ethics
approval from our university.

Figure 1. The example with the 14 salient features we monitored. Note: The
example was given to the participants without the callouts.

###  3.1. Study Design and Materials

The experimental task consisted of a visual ideation activity in which
participants were asked to devise as many ideas as possible for a new chatbot
avatar by sketching them on paper. The written design brief given to
participants was:

> “Your task is to design a character we plan to use as an avatar for a
> chatbot. This chatbot is kind, loving, caring, and intelligent. It can
> assist you in solving your problems and is always there for you to talk to
> whenever you need to. So, imagine that you are conversing with this chatbot
> in real life and then come up with as many sketches as possible. Remember,
> you can annotate the sketch if you need to explain more about your design.
> And please always number each sketch you draw in the order you come up with
> them.”

This written design brief included an example of an avatar with the figure
caption ”Example chatbot avatar (for reference only)”. The example avatar is
shown in Figure 1.

Further, we provided verbal instructions for the participants, asking them to
produce as many different ideas as they could during the experiment. For
participants in the Image search and Gen AI conditions, we additionally
informed them that they could use the digital tool (either Google Image Search
or Midjourney, depending on the condition) to gather inspiration for their
work. The full study protocol can be found in supplementary material.

Similar to previous work (Jansson and Smith, 1991), we started the task by
showing participants an example avatar to induce design fixation. We drew
inspiration from Ward’s creature invention task (Kozbelt and Durmysheva, 2007;
Ward, 1994), which asked participants to imagine and create animals that lived
on a different planet. The authors of this paper created the example chatbot
avatar after several design iterations. We created the avatar so that it had
14 salient features, which we used to quantitatively assess design fixation
(see Figure 1). We considered the presence of these features in participants’
ideas to be evidence of design fixation, following standard practice in the
literature (Jansson and Smith, 1991). In the experimental task, participants
were given 20 minutes to sketch their ideas for addressing the brief. We chose
this time limit because it is the median time given to participants in
previous design fixation studies (Vasconcelos and Crilly, 2016) and because we
aimed to cap each experimental session at one hour to avoid fatigue. We
provided participants with pencils, pens, felt pens, and coloured pencils,
along with blank A4 sheets to sketch their ideas. A timer was placed outside
their peripheral view for them to keep track of time.

The experiment included a single between-participants independent variable—the
Inspiration Stimulus available during the task—with three levels:

  * •

Baseline: no inspiration support.

  * •

Image Search: Participants had access to Google
Images555https://images.google.com/ during the task, accessed through a web
browser in incognito mode to avoid the browser history influencing results.

  * •

GenAI: Participants had access to the paid version of Midjourney V4, an AI
image generation tool, through a private Discord server running the Midjourney
bot (which was required to enter prompts and view outputs from the model).
Midjourney V4 was the default model when our study was conducted (May
2023)666https://www.midjourney.com. Participants interacted with Midjourney
through textual prompts that the model used to generate sets of four images
per prompt.

Figure 2. Examples of sketches created by participants in each experimental
condition. (A) No support condition, (B) Image search condition, (C) GenAI
condition

We assessed participants’ creative output using four standard measures from
the design fixation literature: design fixation, fluency, variety, and
originality, which we describe as follows:

Design fixation is the unintentional conformity towards existing ideas or
concepts that limits exploration of the ideation space (Jansson and Smith,
1991; Youmans and Arciszewski, 2014b). Researchers use the degree of copying
as a method to quantify design fixation (Jansson and Smith, 1991; Moreno et
al., 2016). Therefore, we operationalise design fixation as an objective
property of each sketch based on the presence or absence of features available
in the example. Following the approach used in design fixation literature
(Moreno et al., 2016), two raters blind to the experiment’s aims counted the
presence of features from the example avatar in the sketches created by the
participants. We validated the ratings by computing the inter-rater
reliability and computed the design fixation score (DFS) as follows:

(1) $\begin{split}\text{Design fixation score}=\frac{\text{Number of features
repeated from the example}}{\text{Number of fixating features in the example
}}\\\ \end{split}$

Fluency refers to the number of ideas produced by the participants (Guilford,
1956; Vasconcelos et al., 2016). We operationalise it by counting the number
of sketches produced by each participant within the available time (20
minutes).

(2) $\begin{split}\text{Fluency}=\text{Number of sketches produced by the
participant}\\\ \end{split}$

Variety measures the coverage of the solution space explored during the idea-
generation process (Shah et al., 2003). It aims to capture the extent of the
design space covered during ideation. If the majority of ideas are similar, it
indicates less variety. To compute variety, we assigned a numerical identifier
to all the sketches (N=277), imported them into a Miro777miro.com (an online
collaborative whiteboard), and displayed them in randomised order. Two raters
(blind to the conditions) iteratively and inductively grouped similar sketches
into mutually exclusive clusters. This activity considered several factors:
appearance, embodiment, appendages, shape, and accessories. The process
resulted in 83 clusters. Each participant received a Variety score based on
the number of clusters their sketches were classified into. We subtract 1 from
the number of clusters so that if all of a participant’s sketches belong to
the same cluster, their score is 0, and if they have sketches in every
cluster, their score is 1.

(3) $\begin{split}\text{Variety}=\frac{\text{Number of clusters that a
participant's sketches belong to - 1}}{\text{Number of clusters - 1}}\\\
\end{split}$

Originality (also called Novelty (Fiorineschi and Rotini, 2023; Shah et al.,
2003)) refers to the uniqueness of a particular sketch within the total pool
of sketches made by participants (Jansson and Smith, 1991; Guilford, 1956). It
measures how unusual and unexpected a given idea is. Intuitively, the more
people have the same idea, the less original it is. We computed an idea’s
originality by counting the number of other participants who had an idea
belonging to the same cluster, dividing it by the total number of other
participants, and computing its complement to 1 (to normalise the value
between 0 and 1). In other words, it is the proportion of other participants
who did not have the same idea. This score is 0 when every participant had an
idea in the same cluster and 1 if only a single participant had an idea in
that cluster.

(4) $\begin{split}\text{Originality}=1-\frac{\text{Number of other
participants with ideas in the cluster}}{\text{Number of other
participants}}\\\ \end{split}$

Figure 3. The overall experiment flow 1: Initial briefing and participant
consent, 2: Pre-study questionnaire, 3-7: Main experimental task, 8: Post-
study questionnaire, 9: Semi-structured interview and debriefing

###  3.2. Participants

We recruited 60 participants through digital student notice boards, mailing
lists of university student clubs, and word of mouth. Participants expressed
their interest through a digital signup form. Participants self-described
their prior experience in visual design (measured in years/months). We did not
specify this experience should only be professional design experience. We
screened participants based on our eligibility criteria and invited those who
were 18 years or older and had experience in visual design via email. Further,
to avoid dependent relationships, we ensured that none of the participants had
a direct connection with the primary researcher running the study.
Participants had a mean age of 25.8 years (18–49, SD = 5.4). They included
undergraduate, master’s and PhD students from diverse domains such as arts,
business, computer science & IT, design, engineering, and science. Each
condition had an equal number of participants and was gender-balanced, with 10
women and 10 men per condition (gender was self-described by participants).

###  3.3. Procedure

Participants booked a time to participate individually based on their
availability. The study was carried out in a quiet research laboratory. Upon
arrival, participants read a plain language statement describing the study and
consented to participate (Figure 3-1).

The experiment had four stages: pre-study questionnaire, main experimental
task, post-study questionnaire, and semi-structured interview. Each session
lasted 45–60 min in total. In the pre-study questionnaire, we collected
participants’ basic demographic information, their experience with similar
design tasks (measured in years/months), and their familiarity with AI image
generators (a yes/no question, and participants were asked to list any systems
they had used if they answered yes). (Figure 3-2). The main objective of this
questionnaire was to understand and control for any variables that might
confound the results. After completing the questionnaire, the participants
were randomly assigned to one of the three conditions and were assigned a
unique ID generated by the computer (3 random digits) (Figure 3-3).

In the main experimental task (Figure 3, steps 3-7), participants in all
conditions received the same design brief, which asked them to design an
avatar for a chatbot in 20 minutes, as described in Section 3.1. We started by
allowing participants to familiarise themselves with the available materials.
Then, the participants assigned to the image search and AI-supported groups
received an introduction to the tool they would use during the design task
(Figure 3-5). These tools were available for them to use on an Apple MacBook
M1 Pro laptop. The tool introduction included a video tutorial created by the
research team. This video tutorial explained how to use the tool. After the
video tutorial, we allowed participants to ask questions and clarify any
doubts.

We provided task instructions to participants both verbally and as a written
brief. The written brief included an example of a chatbot avatar, which served
as a stimulus to induce design fixation (Figure 3-6). Participants were given
20 minutes to complete the design task (Figure 3-7). We limited the design
task to 20 minutes to minimise the possibility of fatigue and because previous
studies considered it an ideal duration for maintaining focus for producing
ideas with both quality and quantity (Vasconcelos and Crilly, 2016; Youmans,
2011b). Once participants indicated they were ready to start, the researcher
started the screen recording with participants’ consent (in Image Search and
GenAI conditions), switched on the timer and left them alone to work in the
room, allowing them to work independently.

After the design task, the researcher entered the room and asked the
participant to fill in the post-study questionnaire (Figure 3-8). As the post-
study questionnaire, we administered the NASA-TLX (Hart and Staveland, 1988)
to ensure that all conditions induced an equivalent workload. To analyze the
NASA-TLX, we used a one-way ANOVA; the effect of the independent variable
”condition” on the NASA-TLX overall score was not statistically significant
(F(2, 57) = 1, p = 0.37). Therefore, we did not conduct post-hoc tests.

Then, the researcher conducted a semi-structured interview. Each semi-
structured interview lasted 15–20 min. Through the semi-structured interview,
we aimed to get insights into the participant’s background and their past
experience in creating logos and avatars. We also probed for possible feelings
of design fixation during the experiment and how it was affected by their
previous knowledge, experience and process. In addition, we asked questions to
understand how the stimuli (or lack thereof) affected their ideation process.
To conclude the study, we debriefed the participants about the purpose of the
research. We thanked each participant with a $20 gift voucher.

Figure 4. An example of visual sequence board (A): Participant information and
meta data, (B): AI image generation sequence, (B1): Image generation number,
(C): AI-generated images in the order 1-2-3-4, (D): Prompt used for each
generation, (E): Participant sketch sequence.

###  3.4. Data Preparation

We scanned all the sketches participants created and assigned them a unique
identifier. Two independent evaluators rated the sketches to compute the
design fixation score, variety, and originality measures. These evaluators
were researchers from the human-computer interaction domain with experience in
teaching and evaluating design.

We extracted all prompts and images from the Midjourney gallery where the logs
were saved (not visible to the participants), compiled the sketches and
arranged them in the sequence in which they were created as a visual sequence
board. Underneath the AI-generated images, we added the sketches of the
participants (Figure 4).

###  3.5. Data Analysis

We used a mixed-method approach for our analysis. For quantitative analysis of
design fixation and divergent thinking, we built Bayesian statistical models
to quantify relationships between our dependent and independent variables (see
subsection 4.1). We employ Bayesian statistical methods to analyze our
results, opting for this approach due to its added flexibility, capability to
quantify uncertainty, better handling of small samples, and greater potential
for future extensibility. For a comprehensive rationale advocating the use of
Bayesian methods over traditional frequentist statistics in the field of
Human-Computer Interaction (HCI), see Kay et al. (Kay et al., 2016). Readers
who may not be familiar with these methods can find a beginner-friendly
introduction in McElreath (McElreath, 2020) and can see examples of their
practical application in HCI in Schmettow (Schmettow, 2021). In this manner,
we shift the focus away from p-values and dichotomous significance testing,
directing our discussion towards causal modelling and parameter estimation.

For qualitative analysis of participants’ interview data, we used Braun and
Clarke’s 6-phase approach to reflexive thematic analysis (Terry and Hayfield,
2021; Braun and Clarke, 2022). The analysis was inductive, i.e. data-driven,
based on transcripts of the interviews. Each phase of the analysis was
progressed using NVivo12888https://lumivero.com/products/nvivo for coding
procedures, theme development and naming. The analysis aimed to understand
potential causes of design fixation during the experiment and participants’
approaches to creating sketches in each condition. In this paper’s findings,
we use interview quotes to illustrate participants’ approaches to prompt
creation and their stated approaches to ideation based on AI images. This
enables us to probe plausible explanations for observed differences between
experimental conditions and explore why particular kinds of sketches were
created in response to AI-generated images.

##  4\. Results

###  4.1. Statistical Analysis

Figure 5. Theorised causal directed acyclic graph.

We summarise our theoretical claims as a directed acyclical graph (DAG) in
Figure 5. We argue that the Inspiration Stimulus affects users’ Design
fixation, Fluency, Variety, and Originality. The choice of inspiration
stimulus affects how much time is spent on the sketching task (as opposed to
seeking inspiration), which, in turn, affects the number of sketches produced
(fluency). Higher fluency is also likely to lead to higher variety—as
producing more sketches also increases the likelihood that they will cover
more ground during ideation. A greater variety of sketches, in turn, will
likely lead to more original ideas.

We used the brms package (Bürkner, 2017) to fit our models. This package
facilitates the implementation of Bayesian multilevel models in R, leveraging
the Stan probabilistic programming language (Carpenter et al., 2017). To
ensure the reliability of our Bayesian Markov Chain Monte Carlo (MCMC)
sampling process, we assessed convergence and stability through two metrics:
R-hat, which ideally should be less than 1.01 (Vehtari et al., 2021), and the
Effective Sample Size (ESS), which should ideally exceed 1000 (Bürkner, 2017).
All of our model estimates met these criteria. We built our models based on
the original count data in the direct measurements but report normalised
values as described in Section 3.1 in our plots for easier comparisons with
future work.

In our reporting of model results, we present the posterior means of parameter
estimates, their corresponding standard deviations, and the boundaries of the
89% compatibility interval, often referred to as the credible interval. The
choice of an 89% compatibility interval aligns with the recommendation by
McElreath (McElreath, 2020) to mitigate potential confusion with the
frequentist 95% confidence interval, as the two intervals have distinct
interpretations. The compatibility interval specifies the range of values
within which there is an 89% probability that the true value lies. We report
hypothesis test results using Bayes Factors, which compares the likelihood of
the observed data under the proposed model over the null. We interpret these
values following Wagenmakers et al. (Wagenmakers et al., 2011), considering
values above one as supporting a given hypothesis, values under 3 offering
anecdotal evidence; under 10, substantial evidence; under 30, strong evidence;
under 100, very strong evidence; and above 100, extreme evidence. We note that
p-values are not used in Bayesian statistics, and no claims about “statistical
significance” should be derived from our results.

###  4.2. Design Fixation

To model Design Fixation, we consider the number of salient features in
participants’ sketches also found in the example avatar provided at the
beginning of the experiment. We model this data as a binomial distribution
with N = 14 (the maximum number of features) and a probit link. We use weakly
informative, regularising priors for the model parameters (drawn from a normal
distribution with mean zero and standard deviation of 2). We model the random
effects of participants and images as being drawn from a normal distribution
with mean zero and standard deviation computed from the data through partial
pooling.

Table 1. Summary of the binomial model for design fixation: DFS|trials(14) $\sim$ Stimulus + (1|Participant ID) + (1|Image ID). We provide the posterior means of parameter estimates (Est.), posterior standard deviations of these estimates (SD), and the bounds of their 89% compatibility interval. We note that this is not the same as the frequentist confidence interval but a percentile of the posterior distribution. All parameter estimates converged with an ESS well above 1000 and an R-hat of 1.00. Parameter | Est. (SD) | 89% CI
---|---|---
Intercept | -.71 (.10) | [-.86, .56]
Image Search | .27 (0.14) | [.05, .48]
GenAI | .32 (0.13) | [.11, .54]
Figure 6. Model posterior predictions for Design Fixation scores. Error bars
represent the standard error of the estimates. Scores correspond to the
percentage of salient features in the example found in participants’ sketches
(higher is worse).

The model suggests that the effect of GenAI has a 100% probability of leading
to higher Design Fixation (mean = .32, 89% CI [.11, .55]), and a Bayes Factor
of 124 suggests extreme support for the hypothesis that inspiration from GenAI
leads to higher Design Fixation than the baseline. The effect of Image Search
on Design Fixation was also detrimental, but less so (mean = .27, 89% CI [.05,
.49]), with a 98% probability of this effect leading to higher Design
Fixation. A Bayes Factor of 42.48 suggests very strong evidence for the
hypothesis of a higher Design Fixation than the No support baseline. In
summary, our model suggests that, on average, both stimuli led to more
features in common with the example avatar, and GenAI led to even more design
fixation than Image Search.

###  4.3. Fluency

To model Fluency, we consider the number of sketches produced by each
participant. Our causal model considers two effects of the stimulus on
Fluency: a direct effect and an effect mediated by Time on task (ToT). We
model these effects through two models, with and without Offset(log(ToT)) as a
covariate. This approach was taken to account for varying time on task as
participants of both GenAI and Image search utilised different times to
sketch. In both cases, we model the expected value for each response as a
negative binomial distribution with a log link. This models the sketch count
based on the mean and the shape parameter, both of which depend on the
inspiration stimulus. We opted for this model instead of a Poisson model due
to its ability to model overdispersion. We use weakly informative,
regularising priors for the model parameters (drawn from a normal distribution
with mean zero and standard deviation of 2).

Table 2. Summary of the negative binomial models for Fluency—Direct effects: Fluency $\sim$ \+ offset(log(Time on Task)) and Total Effects: Fluency $\sim$ Stimulus. We provide the posterior means of parameter estimates (Est.), posterior standard deviations of these estimates (SD), and the bounds of their 89% compatibility interval. We note that this is not the same as the frequentist confidence interval but a percentile of the posterior distribution. All parameter estimates converged with an ESS well above 1000 and an R-hat of 1.00. | Direct Effects | Total Effects
---|---|---
Parameter | Est.(SD) | 89% CI | Est.(SD) | 89% CI
Intercept | -1.48 (.19) | [-1.79, -1.17] | 1.50 (.19) | [1.20, 1.80]
Image Search | -.22 (.25) | [-.63, .17] | -.49 (.25) | [-.88, -.10]
GenAI | .10 (.31) | [-.39, .58] | -.21 (.30) | [-.66, .26]
Interceptshape | .75 (.45) | [.04, 1.47] | .77 (.46) | [.04, 1.49]
Image Searchshape | 1.58 (1.15) | [-.01, 3.61] | 1.66 (1.14) | [.06, 3.74]
GenAIshape | -.51 (.61) | [-1.49, .47] | -.40 (.65) | [-1.41, .66]
Figure 7. Model posterior predictions for Fluency (number of sketches
generated by each participant). Error bars represent the standard error of the
estimates.

In the total effects model, both Image Search and GenAI demonstrate
detrimental effects on Fluency. Specifically, for GenAI, there is a notable
negative impact on Fluency with an estimate of -.21 (89% CI [-.7, .28]) and a
Bayes factor of 3.20, suggesting a 76% posterior probability of a negative
effect. This is a substantial indication of its negative total effect on
Fluency. The effect of Image Search is more pronounced (mean = -.49, 89% CI
[-.9, -.10]) with a Bayes factor of 46.62, indicating a 98% posterior
probability of a negative effect, strongly supporting its detrimental
influence on Fluency.

In the direct effects model, which accounts for the time-on-task, the impact
of Image Search on Fluency is minimal (mean = -0.22, 89% CI [-0.64, 0.19])
with a Bayes factor of 4.20, indicating an 81% posterior probability of a
negative effect. In contrast, GenAI shows a relatively neutral direct effect
on Fluency (mean = .10, 89% CI [-.41, .60]) with a Bayes factor of .61,
implying only a 38% posterior probability of a negative effect.

In summary, neither Image Search nor GenAI enhanced Fluency compared to the
baseline, with both generally resulting in lower Fluency. The effect of Image
Search on Fluency is minimal when controlling for total output time,
indicating a less direct impact. However, GenAI does not exhibit a
considerable direct negative effect on Fluency, highlighting its influence is
not strongly dependent on the total time available for sketching.

###  4.4. Variety

To model Variety, we consider the number of clusters a participant’s sketches
belong to minus one to account for the fact that variety only begins with the
second sketch. Our causal model considers two effects of the stimulus on
Variety: a direct effect and an effect mediated by Fluency. We model these
effects through two models, with and without Fluency as a covariate. In both
cases, the expected value for each response is based on a negative binomial
model with a log link. We use weakly informative, regularising priors for the
model parameters (drawn from a normal distribution with mean zero and standard
deviation of 2 for coefficients and a gamma distribution with parameters set
to 0.01 for the shape).

Table 3. Summary of the negative binomial models for Variety—Direct effects: Variety $\sim$ Stimulus + Fluency and Total Effects: Variety $\sim$ Stimulus. We provide the posterior means of parameter estimates (Est.), posterior standard deviations of these estimates (SD), and the bounds of their 89% compatibility interval. We note that this is not the same as the frequentist confidence interval but a percentile of the posterior distribution. All parameter estimates converged with an ESS well above 1000 and an R-hat of 1.00. | Direct Effects | Total Effects
---|---|---
Parameter | Est. (SD) | 89% CI | Est. (SD) | 89% CI
Intercept | .00 (0.23) | [-.38, .36] | 1.01 (.19) | [.71, 1.30]
Image Search | .02 (0.25) | [-.38, .42] | -.39 (.28) | [-.84, -.06]
GenAI | -.15 (0.24) | [-.53, .23] | -.29 (-.28) | [-.74, .17]
Fluency | .14 (0.02) | [.11, .18] |  |
Figure 8. Model posterior predictions for Variety (percentage of clusters in
which the participant has sketches). Error bars represent the standard error
of the estimates.

The total effects model shows a detrimental effect of both stimuli on Variety.
The model suggests that the effect of GenAI has only an 86% probability of
being negative (mean = -.29, 89% CI [-.75, .16]), and a Bayes Factor of 5.93
provides substantial evidence for the hypothesis that it yields a negative
total effect on the variety of output. The effect of Image Search was even
more negative (mean = -.40, [-.87, -.07]), with a Bayes Factor of 11.38
providing strong support for the hypothesis that it has a negative effect.

The model including Fluency as a covariate, models the direct effect of the
stimulus on the Variety of the output. Comparing the two models, we see that
after accounting for the number of sketches that the participant produced,
Google Image Search did not have much of an effect on Variety (mean = .02
[-.39, .43]. However, GenAI still had a negative effect (mean = -.15 [-55,
.24]), with a Bayes Factor of 2.64 suggesting anecdotal evidence for this
effect being negative. In summary, neither Image Search nor GenAI provided
meaningful support over the baseline in terms of enhancing the variety of the
output, yielding, on average, lower variety than the baseline. The effect of
Image Search was fully mediated by Fluency, but GenAI also had an additional
negative direct effect on Variety.

###  4.5. Originality

To model Originality, we consider the number of other participants with
sketches in the same cluster as each sketch. As in the case of Variety, our
causal model considers two effects of the stimulus on Originality: a direct
effect and an effect mediated by Variety. We model these effects through two
models, with and without Variety as a covariate.

Table 4. Summary of the linear regression model for Originality—Direct Effects: Originality $\sim$ Stimulus + Variety + (1|Participant ID) and Total Effects: Originality $\sim$ Stimulus + (1|Participant ID). We provide the posterior means of parameter estimates (Est.), posterior standard deviations of these estimates (SD), and the bounds of their 89% compatibility interval. We note that this is not the same as the frequentist confidence interval but a percentile of the posterior distribution. All parameter estimates converged with an ESS well above 1000 and an R-hat of 1.00. | Direct Effects | Total Effects
---|---|---
Parameter | Est. (SD) | 89% CI | Est. (SD) | 89% CI
Intercept | .83 (0.02) | [.81, .86] | .86 (.01) | [.84, .88]
Image Search | -.01 (0.02) | [-.03, .02] | -.01 (.02) | [-.04, .01]
GenAI | -.03 (0.02) | [-.05, -.01] | -.03 (.02) | [-.06, -.01]
Variety | .01 (¡0.01) | [.00, .01] |  |
Figure 9. Model posterior predictions for originality (percentage of other
participants who did not have an idea in the same cluster of an idea, averaged
per participant). Error bars represent the standard error of the estimates.

The expected value for each response is based on a linear regression model.
This models the originality score based on the inspiration stimulus (and
variety score in the direct effects model), as well as a random effect of the
participant. We use weakly informative, regularising priors for the model
parameters (drawn from a normal distribution with mean zero and standard
deviation of 2 for coefficients). We modelled our random effects as being
drawn from a normal distribution with mean zero and standard deviation
computed from the data through partial pooling.

The total effects model suggests that both stimuli had small negative effects
on Originality. The model suggests that the effect of GenAI has a 97%
probability of being negative (mean = -.03, 89% CI [-.06, .00]), and a Bayes
Factor of 35. provides extreme evidence for the hypothesis that it yields a
negative effect on the variety of output. The effect of Image Search was
slightly less negative but also rather small (mean = -0.01, [-.04, .01]), but
a Bayes Factor of 3.9 provides only anecdotal evidence against the hypothesis
that it has a positive effect. Adding Variety did not change the model in any
meaningful way, suggesting that Variety does not mediate an effect on
Originality. In summary, neither Image Search nor GenAI provided a
considerable aid in terms of developing Originality of the output, offering,
on average, lower originality than the baseline, but these effects were
negligible.

Table 5. Frequency of words used in the prompt by the participants in GenAI condition. Word | Length | Count | Weighted percentage | Similar words | Included in the brief
---|---|---|---|---|---
robot | 5 | 38 | 10.80% | robot, robots | No
kind | 4 | 27 | 7.67% | kind | Yes
chatbot | 7 | 24 | 6.82% | chatbot | Yes
intelligent | 11 | 20 | 5.68% | intelligent | Yes
cute | 4 | 20 | 5.68% | cute | No
caring | 6 | 19 | 5.40% | caring | Yes
loving | 6 | 17 | 4.83% | love, loving | Yes

###  4.6. Why did ideating with Generative AI cause design fixation?

The results from our statistical models suggest that support from Generative
AI led to higher design fixation. To understand why this occurred, this
section draws on our interview data, the prompts created by participants, the
AI-generated images, and the participants’ sketches. We first explore the
content of participants’ prompts as one potential cause of design fixation.
This encapsulates how participants claimed to develop the prompts and how they
were influenced by the design brief or the example design. Next, we
quantitatively explore the similarity between participants’ sketches and the
AI images used to inform that sketch in terms of design fixation. We then
discuss the types of AI-generated images returned by participants’ prompts and
the sketches created based on them, using a case-study-based approach to
illustrate our claims.

Overall, our analysis indicates that participants frequently relied on prompts
containing keywords copied directly from the design brief or used prompts
inspired by the example design. These prompts resulted in AI-generated images
that were conceptually similar to the example design in 44% of cases and which
frequently contained fixating features that were present in the example
design. Further, while not all sketches exhibit high similarity to the example
we provided, ideating based on AI images can lead to fixation displacement,
where participants simply fixate on the images generated by the AI and copy
what they see. This can occur irrespective of whether the participant imitates
the example design or whether they attempt to explore other areas of the
conceptual space.

####  4.6.1. “I just took the words from the brief”: Fixation from prompts
based on the brief and example design

One plausible source of design fixation in our experiment is the prompts that
participants used to generate AI images. That is, if prompts include terms
that are closely related to the example design or which draw from the design
brief, then they might conceivably give rise to AI-generated images that are
similar.

To investigate this possibility, we first analysed the prompts that
participants used for generating images. Participants created a total of 117
prompts, with a mean of 5.85 prompts per participant (median = 5.5 range =
2–15). The length of each prompt ranged from 1 to 26 words (mean = 3.5 words).
To explore the content of the prompts, we conducted a simple word frequency
analysis using the automated word counting feature in NVivo12. This feature
enables us to identify the total number and frequency of unique words that
appear in the prompts. Table 5 shows a summary of the most frequent words
appearing in participants’ prompts.

This analysis revealed that participants frequently created prompts by using
keywords copied from the design brief. In total, 52 prompts (44%) contained at
least one word that appears in the brief. Examples included kind, which
appeared in 27 different prompts; chatbot, which appeared in 24 prompts;
intelligent, which appeared in 20 prompts; and caring, which appeared in 19
prompts. During the interviews, participants reported using this approach due
to a feeling of being ‘stuck’ when trying to develop a prompt. Others
attempted to generate ideas that met the requirements of the design brief.
GenAI-P253, for example, described adapting content from the brief into his
prompt and told us that the process he followed was to “ read the brief, take
the descriptions that they had, and make sure that I was meeting those
descriptions.”

A second approach involved participants using keywords that were themed around
the example design. In total, 57% (67/117) of the prompts contained the word
robot, chatbot or chatbox (a homophone of chatbots).

This suggests that participants often translated what they saw into a prompt
before ideating based on the results. In addition, 78 prompts (66.6%) included
terms related to robots alongside words from the design brief. For example,
P437’s very first prompt was ‘kind loving caring robot’ whereas GenAI-P253
entered ‘cute kind chatbox character’.

Data from the interviews also supports the notion that participants created
prompts which were fixated on the example design. GenAI-P605, for example,
described their process as starting with ‘robots’ and then trying to factor in
other aspects of the design brief. He said that he, “searched up intelligent
robots. But all those robots that I saw in the [AI-generated images], they
looked intelligent, but they didn’t look kind or caring. [I thought], how can
I make it both caring and intelligent?”. This participant created three
distinct prompts: Intelligent robot, Caring robot, and Baymax (referring to an
inflatable computerised robot from a Disney movie) in an attempt to come up
with alternative ideas.

However, it is worth noting participants created 39 prompts that did not
contain words from the brief or phrases related to robots. These prompts
evince participants’ attempts to explore different possibilities within the
conceptual space of a ‘kind and loving’ character. GenAI-P166, for example,
recounted how they started the task by reading the brief and thinking about
what to draw. This led them to the idea of ‘family’, which they then
translated into three distinct but related prompts: family, mom, and Mom -
young. They subsequently drew a sketch of a woman’s face as their only design
after seeing the images Midjourney returned from these prompts.

Taken together, these cases illustrate how creating prompts based on the brief
and the example design may be an initial stimulus for fixation. A successful
strategy to overcome this problem was to try to think ‘beyond’ the brief and
the example. This latter quality is what may be needed from AI systems that
truly support designers in avoiding fixation.

####  4.6.2. “I would just kind of copy it and then tweak”: AI-generated
images as a cause of fixation

A second putative cause of design fixation in our experiment is the AI imagery
that participants saw. That is, if the AI images were not meaningfully
different to the example design, then this may have encouraged fixation
because participants did not consider (or simply were not exposed to) other
possible alternatives. This explanation is plausible given that 66.6% of all
prompts contained terms related to robots or words copied from the design
brief. Prompts containing these terms might be expected to produce images
similar to the example design, in turn leading to fixated sketches.

To explore the relationship between fixation in participants’ sketches and the
AI images, we first computed the correlation between the design fixation score
of participants’ sketches (previously calculated by two independent raters,
see Section 3.1) and the design fixation score of the most recent set of AI-
generated images immediately preceding each sketch. We selected Spearman’s
rank correlation (a non-parametric test) as the data did not satisfy normality
assumptions.

For this analysis, we begin with the total set of sketches produced by
participants in the AI condition (92 in total). We found that 10 of these
sketches were created prior to entering any prompts into Midjourney; we,
therefore, removed these sketches from consideration as there are no
equivalent AI images to compare them against. This left us with 82 sketches,
which we plotted against the relevant AI-generated images seen immediately
before drawing the sketch.

Figure 10. Scatterplot illustrating the correlation between the design
fixation score of GenAI images appearing immediately before a participant’s
sketch and the design fixation score of the sketch associated with the same
set of GenAI images. DFS = design fixation score.

Figure 10 illustrates the correlation. We observed a moderate positive
correlation between the design fixation score of each sketch and the design
fixation score of the AI images immediately preceding that sketch ($\rho$ =
0.56). This provides quantitative support for the idea that AI-generated
images that contained features of the example avatar led to sketches with
higher design fixation scores.

Figure 11. An example of a participant producing sketches based on AI images
that are similar to the example design, evidencing fixation when co-ideating
with AI.

Next, we qualitatively investigated what kinds of images were generated by
Midjourney in response to participants’ prompts and whether the resulting
sketches were fixated on these images. We began with a simple visual
inspection of the AI images to probe whether Midjourney’s outputs were
meaningfully different to the example design.

This inspection revealed that 44% (206/468) of the AI-generated images
portrayed humanoid robots that were conceptually similar to the example
avatar. In turn, these images were qualitatively similar to the sketches
participants made in response to them, indicating a tendency among
participants to imitate — or even directly copy — what they saw.

Figure 11 shows an example of this phenomenon. The figure shows the AI images
seen by one participant in Midjourney over time. It then positions the
participant’s sketches according to the most recently issued group of AI
images before the sketch was drawn. In this example, it is immediately evident
that the majority of sketches are superficially similar to the images returned
by Midjourney. Likewise, these sketches are similar to the example design we
provided (i.e. a cutesy robot) and typically contain the same salient features
(legs, arms, and so on). The presence of these features and their inclusion in
the subsequent sketches is one plausible explanation for why the AI support
did not encourage participants to ‘break free’ of fixation. It appears to have
merely reinforced the existing problem.

This phenomenon arose irrespective of whether participants ideated on the fly
or considered multiple ideas before creating a sketch. Figure 12 illustrates a
second case where the participant is once again fixated on the idea of a
robot. In this instance, the participant delays sketching until after issuing
multiple prompts and seeing several rounds of AI images. It can be seen that
the single sketch the participant created is of a robot-type character,
evidencing fixation. The sequence also highlights how the prompt plays a role
in this effect, with the participant attempting to vary their initial
‘chatbot’ prompt by adding keywords such as ‘intelligent’ or ‘kind’, but
resulting in thematically similar returns each time.

Figure 12. A second example of a fixated sketch created after several rounds
of prompting and image generation. In this case, the prompt is also fixated on
the idea of a chatbot, creating similar returns from Midjourney each time.

Cases such as these are illustrative of how fixation may have occurred, with
participants repeatedly generating ideas that were similar to the example
design and imitating the ideas within them. The interview data supported this
latter idea. When asked about their approach to ideation, participants
described the AI as a “source of inspiration” but openly admitted they
sometimes copied what they observed. For example, GenAI-P253 claimed that
using AI “helped a lot of the inspiration for a lot of the designs that very
much I just put down what I wanted it to give me, and I would just kind of
copy it and then tweak it a little bit for the designs. ”

Overall, these cases highlight the risk of AI simply reinforcing the
phenomenon of fixation on an initial example. In turn, they raise the question
of how AI systems might be usefully designed to encourage shifts away from
this effect.

####  4.6.3. The notion of “Fixation Displacement”

In addition to investigating how fixated sketches resulted from fixated
images, our inspection of the sketches in relation to AI images revealed an
additional phenomenon not well-captured by the correlational analysis. That
is, there is evidence of what we describe as fixation displacement, where the
participant creates sketches with little relation to the original design but
which are very clearly fixated on the AI imagery. Here, the sketches produced
are both objectively and subjectively different to the example design but
demonstrate a high degree of fixation with the AI images.

Figure 13 illustrates an example of fixation displacement in action. Here, the
participant entered the prompt ‘goddess’ as a way of beginning their ideation.
This prompt has little connection to the design brief or the idea of a robot
avatar. The participant then produced a sketch of a woman’s face, which shares
a small number of features with the example robot avatar (eyes, mouth, ears)
but which is qualitatively different. Then, they proceed to iterate on this
idea, resulting in three sketches that are similar in appearance and which
bear a close resemblance to what the participant is seeing in Midjourney.

Figure 13. An example of fixation displacement, this is where the participant
has shifted their sketches away from the example robot avatar but has now
become fixated on the idea of a woman’s face via the AI images. Figure 14. An
example of a participant progressing through different ideas and arriving at a
final sketch that bears no resemblance to the example robot avatar.

Crucially, this phenomenon is not captured in our earlier scatterplot (Figure
10) because the images and sketches have only a few features in common with
the example design. This means they would be rated as quantitatively ‘low’ on
design fixation. In our experiment, fixation is operationalised in terms of
similarity to the example design, where similarity is assessed by the presence
or absence of features from the robot avatar. Conceptually, however, fixation
refers to “blind adherence to a set of ideas or concepts” (Jansson and Smith,
1991). This general phenomenon is clearly depicted by the images and sketches
in Figure 13, highlighting a new and novel risk of employing AI in ideation.
That is, design fixation towards an initial example may not be overcome by
using AI but may simply be displaced onto the examples that the AI provides.
If one operationalises fixation in terms of deviation from an initial example,
one might argue that such an outcome is apposite or even desired. But if one
operationalises fixation in terms of blind adherence to an idea, then this
outcome is questionable.

What one may wish to see from AI-based ideation might be more akin to the
process seen in Figure 14. Here, the participant-generated 8 groups of images
from Midjourney, beginning with prompts (such as ‘dragon’) that have no
relationship with the example design but which might inspire useful ideas and
further exploration of the conceptual space. By the fourth prompt, the
participant latched onto the idea of intelligence, which is then used to
produce an Einstein-themed robot after prompt 6. However, the participant
abandoned this idea and moved to an abstract design which bears no resemblance
to the exemplar. While this still evidences some degree of fixation
displacement, given the sketches the participant produced, it represents a
significant conceptual shift from the example design. That is, the participant
has considered a range of alternatives and has produced a seemingly useful
design that bears no resemblance to the given example. This is perhaps more
indicative of what we would consider to be effective AI-supported ideation.

##  5\. Discussion

This study aimed to identify the effects of using an AI image generator as
inspiration support for an ideation task. Our quantitative analysis revealed
that using AI-generated images had a detrimental effect on participants’
ideation performance. Therefore, we aimed to uncover the cause of this effect
through our qualitative analysis. We identified that AI caused more design
fixation in participants and hindered the variety, originality and fluency of
ideas compared to the baseline condition. Further, we observed a moderate
positive correlation between the design fixation score of participants’
sketches and the design fixation score of AI-generated images, which suggests
that AI has a potential influence in determining the outcome of the ideas.
Further, we observed that AI induced a fixation displacement in participants
where, even if they shifted their focus away from the initial example, they
became fixated on the AI-generated images instead. In this section, we reflect
on our learnings and discuss potential opportunities for developing generative
AI to better facilitate ideation tasks, and propose strategies for improving
divergent thinking during ideation. In doing so, we look at different phases
of the ideation task performed by participants in detail (see figure 15).

Figure 15. The overall ideation workflow of participants in the AI-supported
condition,($A_{1}$): Written brief to the prompt,($A_{2}$): Initial example to
the prompt,($B$): Initial example to the sketch, ($C$): Prompt to AI-generated
images, ($D$): AI-generated images to the sketch

###  5.1. How to avoid fixation on the design brief when determining prompts
for Generative AI?

Through our study, we identified that the prompt acted as a potential source
of design fixation. While participants used different strategies to devise
their prompts (Figure15-A), the results suggest that most of them used
keywords from the brief (Figure15-A1) and built upon the idea of a ‘robot’
from the initial example (Figure15-A2) when devising their prompts.
Participants claimed that they tried to avoid copying the initial example
later during sketching (Figure15-B), but this was not reflected in the data
given that the high design fixation score was calculated based on the ratio of
replicated features from the initial example. Therefore, we assume that poor
prompt design led to the generated images sharing similar features with the
example. This suggests that design fixation happened when the participants
created the prompts (Figure15-A). Participants tended to produce prompts that
were semantically similar to the words given in the design brief and examples,
while exhibiting strategies of repeating the same steps when creating prompts.
Prior work indicates that participants can become fixated on exposed examples
(Jansson and Smith, 1991; Cardoso et al., 2009). Further, studies have shown
that participants tend to fixate on self-generated ideas and concepts compared
to initial examples (Leahy et al., 2020). Our study aligns with these
findings, suggesting that some participants were fixated on the design brief,
the example avatar, or self-generated ideas when determining keywords for the
prompt.

Thus, paying attention to different prompting strategies might help mitigate
this first potential occurrence of fixation when co-ideating with AI. Youmans
et al. (Youmans and Arciszewski, 2014b) summarise different strategies to
mitigate design fixation based on the cause of occurrence. One strategy to
snap participants out of fixation is to have timely warnings to consider
alternative options (Youmans and Arciszewski, 2014b). Therefore, creativity
support systems based on generative AI could not only turn prompts into images
but also scaffold users’ abilities to craft better prompts for ideation. AI
systems could support users in creatively interpreting the brief, push users’
thinking into alternative directions, or mix arbitrary ideas into the prompts.
This functionality could be enabled through other generative AI techniques,
such as large language models.

Cheng et al. (Cheng et al., 2014) have found that showing low-fidelity,
abstract, and partially completed ideas led participants to become more
divergent in their thinking and reduce fixation. Users of AI systems should
consider using prompts that generate low-fidelity abstract or partial images
when interacting with AI because images with these qualities might alleviate
design fixation and encourage divergent thinking in an ideation task. Having a
predetermined prompt structure or template that describes ways to make the
images more abstract and less refined might lower the risk of fixation.

###  5.2. How can AI generate images that better support ideation?

The images generated by the AI system in this study were high in fidelity,
visual detail, and quality; they appeared to be rich in shape, form, texture,
colour, composition, and visual expressiveness (see Figure 11-14). Though this
showcases impressive functionality, it might have amplified conformity towards
the generated image, causing fixation displacement. This aligns with prior
studies, which have shown that complete and strong examples carry the
potential to cause fixation (Cardoso et al., 2009; Smith et al., 1993;
Chrysikou and Weisberg, 2005; Cheng et al., 2014). Previous work has found
that introducing some incubation time can help dissolve concrete exemplars
into more abstract concepts, lowering fixation (Youmans, 2011a; Smith and
Linsey, 2011) and supporting the emergence of novel ideas (Deo et al., 2021;
Sio and Ormerod, 2009). Though this was not possible in our study, given the
short time available for the task, it is a process that users of AI systems
can incorporate into their ideation process. Further, when developing
generative AI to support ideation, it may be useful to introduce mechanisms to
lower the fidelity and the richness in detail of the output. Another direction
is to show partially completed or blurred outputs, which might be beneficial
for introducing ambiguity and pushing ideas in new directions (Cheng et al.,
2014). Recent works by Davis et al. (Davis et al., 2019) and Williford et al.
(Williford et al., 2023) provide initial evidence suggesting that these
mechanisms might be plausible approaches to be embedded in generative AI.

###  5.3. How to translate AI images into design ideas?

Through our visual comparisons, we observed similarities between the sketches
produced by participants and the AI-generated images, suggesting that
participants had imitated and, in some instances, directly copied elements
from the images generated by Midjourney. Further, we identified that
regardless of whether participants ideated on the fly or considered multiple
ideas before creating a sketch, they gravitated towards features of the images
which Midjourney generated, leading to fixation. Copying elements from an
example is the easiest way to result in fixated outputs (Jansson and Smith,
1991; Cardoso et al., 2009), and our findings were consistent with this.

To successfully act as sources of inspiration, generative AI tools must
encourage more strategies that are more effective than copying. Previous work
has shown that techniques like visual analogy—identifying abstract
correspondences between the images being generated and the solution being
sought—can improve the ideation effectiveness of designers of all levels,
including novices (Casakin and Goldschmidt, 1999). However, Casakin and
Goldschmidt highlight that even though novices already have an inherent
understanding of how visual analogy works, they must be shown how to do it
well and how it can support problem-solving in design activities (Casakin and
Goldschmidt, 1999). Scaffolding these skills is a promising role for AI-based
creativity support tools.

We observed lower fluency in the GenAI and Image search conditions. Because
the time given to complete the task was the same in all conditions, there was
an inherent trade-off between spending time producing ideas vs. seeking
inspiration. Interacting with both the AI image generator and the web image
search led to less time spent sketching. These results tally with the findings
of Vishwanathan and Linsey (Viswanathan and Linsey, 2012), who found that
though physical prototyping techniques that required more effort led to higher
quality ideas in an engineering design task, they also increased design
fixation and lowered fluency. They hypothesise that this is due to a “sunk-
cost effect”—the higher the effort spent in a given direction, the harder it
is to move into a different one. Participants who spent more time refining
prompts and interacting with the AI also had worse ideation performance. Users
of generative AI systems should be careful and deliberate in their approach
when seeking inspiration from external stimuli like AI image generators to
mitigate the risk of design fixation. Crilly (Crilly, 2015) suggests that
empowering designers to recognise and reflect upon fixating episodes might be
beneficial in developing a less fixating co-ideation workflow with AI.
Further, Neroni and Crilly (Neroni and Crilly, 2021) state that uncovering
participants’ fixation tendencies, which they call ”demonstrated
vulnerability”, is an effective approach that can further strengthen
participants’ ability to overcome fixation. In summary, when developing
generative AI for co-ideation tasks, there is a rich opportunity for designing
interactions with intelligent agents that not only generate stimuli but also
encourage better ideation behaviours. Triggering timely reminders, suggesting
new idea directions, preempting fixation, varying the abstraction of the
visual outcomes, and facilitating visual analogical reasoning are all
promising directions for future work.

###  5.4. Limitations

We acknowledge several limitations in our study. First, at the time of
writing, generative AI tools are still nascent technologies. Interaction
paradigms are emerging, and users are still learning to leverage their
potential. As such, our results describe a picture of somewhat naïve use of
these tools. It will be interesting to see how these results evolve as users
become accustomed to generative AI tools and incorporate them into their
practice.

Next, in this study, we gave all groups of participants the same amount of
time to complete the task, but we observed lower fluency in the GenAI
condition. We note that when the experiment took place, the AI system did not
produce results instantaneously, which potentially delayed participants in
that condition. However, we also note that participants in the Image Search
condition, who did get their results instantaneously, also exhibited lower
fluency than the baseline. This could be due to the exposure to a large number
of images with endless scrolling, which added another layer of decision making
to pick the ideas that suit them. Therefore, in a real-world setting, it is
important to consider the trade-off between spending time on the task (e.g. by
sketching) vs. seeking inspiration (e.g. by interacting with a creativity-
support tool).

We acknowledge that because we limited the task time to 20 minutes based on
previous work, we restrict the scope of our insights to short-term usage of
these tools in a rapid ideation task. In the real world, people may spend
longer reflecting on the outputs of AI, and incubation time along with
iteration of sketched ideas may produce results different from those of our
experiment. Further, we screened participants for prior skills in visual
design, but few had professional industry experience. Though our sample was
balanced across conditions, we make no claims about how these effects might be
affected by expertise.Therefore, with this study, we can only provide initial
insights into how a novice designer might approach a design task, and to
generalize these claims, we need further investigation.

In this study, we operationalised design fixation by looking for a restricted
set of salient features from the example in participants’ sketches. Though the
choice of focusing on denotative elements of the design aims to facilitate
operationalization, we acknowledge that there are also connotative aspects
that were left outside the scope of our analysis, including art style,
emotional expression, and cultural references. Finally, our study only
evaluated the potential of generative AI tools for ideation support through
the specific example of image generators in a visual ideation task. It remains
to be seen how these effects translate to other modalities, such as text,
video, audio, and music generation.

##  6\. Conclusion

Through this study, we contribute empirical evidence to the discussion of the
potential of generative AI to augment human creativity. Our study revealed
that using an AI image generator as a source of inspiration by novice
designers led to higher design fixation on an initial example and lower
fluency, variety, and originality of ideas compared to using a conventional
image search or no inspiration support. We suggest that fixation can happen in
how the brief and the example influence the prompt given to the AI system, how
the system translates it into images, and how the images inspire participants’
ideas. All of these offer rich opportunities for re-design. Our work suggests
that, at least in the current context of AI tool usage, given a fixed amount
of time for a visual ideation task, this time is better spent sketching than
seeking inspiration through AI. Our work suggests that generative AI tools
aimed at supporting co-ideation should not only focus on generating stimuli
but also on encouraging more effective ideation behaviours. We believe that
incorporating well-thought-out methods and strategies into user practices and
developing generative AI tools that can reduce common obstacles, such as
design fixation and other creativity blockers, can maximise its potential to
speed up the creative process and improve the quality of innovative design
output.

###### Acknowledgements.

This research is supported by the Rowden White Scholarship and the Melbourne
Research Scholarship offered by the University of Melbourne. We also would
like to thank Christian Davey at the Melbourne Statistical Consulting Platform
for their support.

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## APPENDIX

##  Appendix A The Mean Scores and Standard Errors of the NASA Task Load Index
(NASA-TLX) Scales

Table A.1. The mean scores and standard error for each NASA TLX scale (mental demand, physical demand, temporal demand, performance demand, effort demand, frustration demand) in the three conditions: No support, Image Search and GenAI | Mean scores & Std. Error
---|---
NASA TLX | No Support | Image Search | GenAI
Mental Demand | 4.1 (0.3) | 4.0 (0.4) | 4.2 (0.4)
Physical Demand | 2.2 (0.3) | 2.6 (0.3) | 2.6 (0.3)
Temporal Demand | 4.3 (0.4) | 3.7 (0.4) | 4.4 (0.4)
Performance | 4.1 (0.3) | 5.2 (0.3) | 4.7 (0.3)
Effort | 4.5 (0.3) | 4.0 (0.4) | 4.0 (0.2)
Frustration | 3.1 (0.4) | 2.0 (0.3) | 2.2 (0.3)