# AI-Assisted Causal Pathway Diagram for Human-Centered Design

Ruican Zhong  0009-0004-7169-0675<EMAIL_ADDRESS>University of
WashingtonSeattleWAUSA ,  Donghoon Shin  0000-0001-9689-7841<EMAIL_ADDRESS>University of WashingtonSeattleWAUSA ,  Rosemary Meza  0000-0002-4849-7250
<EMAIL_ADDRESS>Kaiser Permanente Washington Health Research
InstituteSeattleWAUSA ,  Predrag Klasnja  0000-0002-4570-703X
<EMAIL_ADDRESS>University of MichiganAnn ArborMIUSA ,  Lucas Colusso
0000-0002-9773-3483<EMAIL_ADDRESS>MicrosoftRedmondWAUSA and  Gary
Hsieh  0000-0002-9460-2568<EMAIL_ADDRESS>University of WashingtonSeattleWAUSA

(2024)

###### Abstract.

This paper explores the integration of causal pathway diagrams (CPD) into
human-centered design (HCD), investigating how these diagrams can enhance the
early stages of the design process. A dedicated CPD plugin for the online
collaborative whiteboard platform Miro was developed to streamline diagram
creation and offer real-time AI-driven guidance. Through a user study with
designers ($N=20$), we found that CPD’s branching and its emphasis on causal
connections supported both divergent and convergent processes during design.
CPD can also facilitate communication among stakeholders. Additionally, we
found our plugin significantly reduces designers’ cognitive workload and
increases their creativity during brainstorming, highlighting the implications
of AI-assisted tools in supporting creative work and evidence-based designs.

causal pathway diagram, human-centered design, generative AI, LLM,
implementation science

††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: 10.1145/3613904.3642179††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Systems and tools for interaction design††ccs: Human-
centered computing HCI design and evaluation methods

##  1\. Introduction

Causal pathway diagram (CPD) is a graphical tool that represents the causal
relationships between variables and the desired outcomes under a specific
context (Lewis et al., 2018; Meza et al., 2023). CPD is a valuable tool in
designing theory-driven behavioral implementation strategies (Lewis et al.,
2018). CPD uses graphical representations to depict the mechanisms in which an
implementation strategy is thought to work (Meza et al., 2023). It helps map
out how an implementation strategy can achieve desired outcomes; highlighting
factors that are necessary or helpful to achieve those outcomes.

Figure 1. An example of causal pathway diagram of increasing clients’ physical
activities

Consider the scenario where designers are tasked with increasing clients’
physical activities (Figure 1). They can use CPD to synthesize their own user
research with published research to map out the factors that can lead to this
desired outcome (Figure 1). Working backward, they can map out relevant
proximal goals (e.g., having clients take the stairs instead of the elevators;
Figure 1), identify barriers that prevent target clients from taking the
stairs (e.g., concerns about not being able to walk up all the stairs; Figure
1) and appropriate mechanisms (e.g., increase self-efficacy; Figure 1) to help
overcome the barriers. They may also use CPD to consider relevant moderators
and preconditions they must consider during design. For example, if the design
strategy they choose to focus on is a low-tech solution of displaying a poster
with positive messaging of “Yes you can!” to help increase clients’ belief
that they can take the stairs, designers may note moderators (e.g., the
credibility of the poster; Figure 1) and preconditions (e.g., whether users
can read and understand the poster; Figure 1) that is important for the design
to function as intended. CPD can also support brainstorming. Designers can
consider other paths that can result in the same distal outcomes, potentially
exploring alternative proximal outcomes, barriers, mechanisms, or strategies.
Finally, with this visualization, designers can articulate the design goals
and why the overall strategy may work for various stakeholders (Figure 1). The
use of CPDs can be valuable throughout a design project: at the start of the
project by identifying relevant factors and mechanisms to prioritize and
optimize; during the implementation by addressing barriers; and at the end of
the project to help diagnose and evaluate outcomes (Lewis et al., 2022).

Though originally developed for implementation science, CPD may be extended to
human-centered design (HCD), as HCD and implementation science share similar
underlying objectives. At the root of both implementation science and human-
centered design is the question of how to ensure that what we design will
ultimately work well; be used and adopted by users to achieve the desired
outcome. There has been increasing interest both within the HCI community and
in the implementation science community to strengthen the exchange of methods
and integration of ideas (Lyon et al., 2020; Dopp et al., 2020; Lyon et al.,
2023). In the same way that an implementation scientist may use CPD to help
design and evaluate implementation strategies, designers may also find value
in using CPD in the earlier stages of design as a way to drive evidence-based
thinking. Designers may use CPD to model their synthesized research findings
into a visual representation of users’ goals and barriers, and identify
relevant contextual factors that can influence design uptake. They may also
use CPD to guide ideation and the development of design hypotheses.

However, despite the potential benefits of CPD, several challenges may exist
that can prevent designers from using and integrating CPD into their work.
Conceptually, the use of CPD involves thinking about the interventions and
strategies that are being designed at a different level of abstraction and
granularity than may come naturally to design practitioners (Lewis et al.,
2018). Instead of directly diving into brainstorming, when using CPD,
designers are challenged to first identify and organize relevant factors in an
evidence-based way. This different design paradigm may be perceived as
difficult to integrate into existing workflows. Further, those who are
unfamiliar with CPD may find it hard to adapt to the CPD components and syntax
(i.e., what is a mechanism and how should it be represented). Finally, at a
practical level, generating these causal diagrams can incur additional costs
in drawing, sharing, and iterating with peer feedback.

In order to address these potential challenges of adopting CPD in HCD, we
developed a CPD plugin for an online collaborative whiteboard, Miro.
Specifically, the plugin provided CPD elements that can be used directly by
the designers. This enables people to focus on the conceptual work in CPD and
minimize time spent on making the visual diagrams. The plugin also included
features that provide guidance on how to develop CPDs and provide real-time
suggestions using generative AI (i.e., GPT-4) to support an exploration of
relevant factors.

We conducted a user study with 20 design practitioners. Participants were
asked to brainstorm solutions for design sprints with the help of CPD and the
plugin. Specifically, we addressed the following research questions:

  * •

RQ1: How do designers use CPD in human-centered design?

  * •

RQ2: How does the plug-in support practitioners in designing with CPD?

We found that designers are positive about integrating CPD in the early stages
of human-centered design, especially for brainstorming and strategic
prioritization. Designers pointed out that CPD is particularly effective
because it emphasizes directly addressing the root cause of the problem as a
goal-oriented design process. We also found that our plugin was able to reduce
the cognitive workload involved in memorizing the CPD framework itself,
allowing people to focus their energy on the design task. Furthermore, with
LLM-generated recommendations, our plugin was able to enhance designers’
creativity in brainstorming sessions. However, designers also emphasized the
potential consequences of using the AI suggestions irresponsibly and
highlighted opportunities to make the AI recommendations more evidence-based
and to communicate the information provenance.

In this work, we offer several important contributions:

  * •

Introduction and study of how practitioners can use CPD in the early stages of
human-centered design

  * •

A diagramming plugin111The codebase and the application of our plugin are
available on the following links: (i) codebase, (ii) plugin (application) that
helps designers generate and iterate on CPDs to guide design

  * •

Insights about the challenges and opportunities of using AI assistance in
supporting creative work and evidence-based designs

##  2\. Related Work

###  2.1. Theory Use in Human-Centered Design

Human-centered design (HCD) is built on the idea that through a better
understanding of people, we can generate more effective designs (Shneiderman
and Plaisant, 2010; Rogers et al., 2002; Carroll, 2003; Benyon, 2013). There
has been a long tradition of using theory to guide design (Rogers, 2004), such
as the use of cognitive science theories to guide the design of computer
interfaces (Carroll, 1991), use of social psychology theories to design online
communities (Kraut and Resnick, 2012), and the use of social and behavior
theories to guide behavior change technologies (Consolvo et al., 2009).

Through their synthesis of prior research (e.g., (Rogers, 2004, 2022; Bederson
and Shneiderman, 2003)), van Turnhout et al. points out six key types of
functions of theory when designing technologies for people (van Turnhout et
al., 2019): (1) Description, to identify phenomena and describe them in a
consistent and clear manner; (2) Explanation, to expose underlying causes and
their relationships; (3) Generative, to support the generation of novel ideas
and design alternatives; (4) Aspirational, to explicate what ideals, goals or
values to strive for in design; (5) Prediction, to predict effects in normal
and novel situations; and (6) Prescription, to provide advice about what (not)
design such as guidelines, and best practices.

Within a typical human-centered design process, these functions may manifest
in different ways. For example, during brainstorming, theories can help by
suggesting ideas for designers to explore. They can support divergent thinking
by identifying space to explore. They could make salient how relevant
strategies influence users and behaviors, and highlight barriers to overcome.
They can help designers generate design hypotheses about the outcomes of their
designs. Theories also help in convergent stages of design by guiding
decisions on which designs to prototype and implement so that the design goals
can be optimized. During evaluation, theories can inform study design and help
interpret findings. At a more meta level, theories can support communication
with stakeholders, both to explain the goal of design as well as to help
justify and get buy-in for design decisions.

However, despite the various benefits of using theory in design practice
(Hekler et al., 2013), many have noted that research and practice gaps exist
(Colusso et al., 2019; Norman, 2010; Gray et al., 2014; Roedl and Stolterman,
2013; Rogers, 2004). Effectively using theories to approach design problems
has faced unique and enduring challenges (Dourish, 2006; Schon, 1983). There
are several critical barriers preventing their usage. First, designers are
often not trained in relevant basic science fields, and consuming and
expressing knowledge in a theory-driven, or evidence-based approach can be
difficult (Beck and Stolterman, 2016; Colusso et al., 2017). Second, even if
designers are familiar with relevant theoretical insights, how to translate
theoretical insights into the “messy” real-world context may not be simple and
straightforward (Rogers, 2004). Finally, when to engage theory may also be
unclear. How to incorporate theories in a format that can fit with existing
design processes is also important for theory usage (Colusso et al., 2017,
2018).

###  2.2. Causal Pathway Diagramming (CPD)

In this paper, we hypothesize that causal pathway diagramming (CPD) may be a
useful tool to guide theory-driven human-centered design. CPDs are box-and-
arrow diagrams that depict “interrelations among variables and outcomes of
interest in a given context.” (Lewis et al., 2018) CPDs have been proposed in
implementation science as a way to support evidence- or theory-based design,
to help overcome the observation that most existing health care and public
health implementation fail to achieve the intended change (Damschroder et al.,
2009). By using CPDs to map out the ways in which interventions could overcome
barriers and how they could improve the outcomes of interest, CPDs could be
applied across different stages of HCD, to help: (1) inform the design and
development of the strategy, (2) support the brainstorming of new strategies,
(3) increase the impact of existing strategies, and (4) help prioritize which
strategies to use in which contexts (Lewis et al., 2018).

Though CPD has only been recently proposed in health implementation, there has
been a long-established history of using these types of diagrams across
various fields. At its abstract form, causal pathways graphically describe
causal relationships within a set of variables, and are widely used in social
and behavioral sciences and statistics (Pedhazur and Kerlinger, 1982). More
recently, the Theory of Change (ToC) (Taplin et al., 2013) has also used the
Outcomes Framework as a way to provide a visual representation of the
preconditions and requirements necessary to achieve a desired goal. The idea
is that by defining the outcome of interest and identifying rationales and
assumptions, the Outcomes Framework can help philanthropies and nonprofits
plan and evaluate systems in a more evidence-based way (Connell and Kubisch,
1998). However, while many have proposed this type of causal mapping to
support human-centered design, its use is still relatively limited. Outside of
global health where there has been an intersection of methods from ToC and HCD
(LaFond and Cherney, 2021), this type of causal pathway diagramming is rarely
referenced as part of the design process in human-centered design and HCI
literature. Thus, one of the research questions is to explore the potential
benefit of using this type of visual representation in human-centered design.

In our work, we use the CPD method that has been developed by the OPTICC
Center (Lewis et al., 2021), a National Cancer Institute (NCI) funded center
focused on optimized evidence-based intervention implementation. Unlike the
Outcomes Framework from (ToC) (Taplin et al., 2013), OPTICC’s CPD method puts
forward a formal syntax (Meza et al., 2023). First is the strategy, or
intervention of focus. It is depicted as a rounded rectangle, and is typically
the leftmost element of a CPD. What follows a strategy element is usually a
mechanism element, which explains how or why the strategy works. Mechanisms
are depicted as diamonds. Another key component of the CPD is the barrier, or
the obstacle in place that prevents the achievement of the desired outcome.
Barriers are depicted as octagons. Outcomes are depicted as circles and are
typically the last element in a CPD. There could be multiple versions of the
outcome circles, including proximal outcomes, intermediate outcomes, and
distal outcomes. Outside of the stem of the CPD, there are also moderators and
preconditions. Moderators are depicted by rectangles, and represent factors
that facilitate or impede a part of the causal process. Preconditions are
depicted as isosceles trapezoids, and represent factors that are necessary for
a part of the causal process. While having more formalized syntax can help
structure and improve the consistency of causal diagrams, it can pose a
barrier to using CPDs. This is on top of needing to learn what causal pathways
are and how to use them. Therefore, our second research question is whether we
can build a tool to facilitate the use of causal pathways in design.

###  2.3. Potential Use of CPD During Ideation

Many tools have been designed to support creativity in HCD (Frich et al.,
2019; Wang and Nickerson, 2017; Gabriel et al., 2016). However, few of these
creativity support tools (CSTs) have been developed with an emphasis on
supporting the generation of ideas in an evidence or theory-driven way. One
set of CSTs simply stimulates designers to think creatively. These tools are
not specific to the design context and often just contain inspirational images
or words (Eames and Eames, 1952; Hsieh et al., 2023), or provocative concepts
(Vines et al., 2012). Others support brainstorming by suggesting additional
relevant ideas based on a set of existing ideas inputted by the user (Clark et
al., 2018; Wan and Lu, 2023; Bunian et al., 2021; Feng et al., 2022; Lu et
al., 2024; Wang et al., 2010; Andolina et al., 2015, 2017; Ferdowsi and Saad,
2023). Often, these tools help surface inspirational stimuli by varying the
analogical distance, commonness, and modality of the example ideas  (Chan et
al., 2011; Lawton et al., 2023; Zhang et al., 2022; Cai et al., 2023;
Zamfirescu-Pereira et al., 2023a). IdeaExpander, for example, draws on the
conversations of group brainstorming and provides recommendations of related
pictures to stimulate a divergent brainstorming process (Wang et al., 2010).
Clark et al.’s work on creative writing suggests related information based on
existing user inputs, which helped with ideating possible slogans and stories
(Clark et al., 2018). VINS is a system that recommends relevant UI examples to
support UI design brainstorming (Bunian et al., 2021). While these tools could
help designers come up with more innovative ideas more efficiently, without
the integration of evidence from user research or published research, it is
unclear if the generated ideas will work when addressing the design problem.

Integrating CPD into creativity support may help scaffold the designers to
consider the relevant evidence or theory throughout–and thus ensuring a higher
likelihood of the generated ideas to be effective (Lewis et al., 2022). CPD
can also be useful during the four key types of CST activities as noted by
Shneiderman: collect, relate, create, and donate (Shneiderman, 2002).
Collecting refers to the process of gathering insights from prior literature
and resources. The creation of a CPD would encourage designers to collect and
utilize research findings and theories in order to examine relevant causal
pathways. Relating refers to consulting with their colleagues and managers
throughout the design process. CPD, as a graphical representation of how a
possible design solution addresses the problem space, could serve as the
microtheory to facilitate the discussions of goals and relevant factors to
consider. Creating refers to the divergent thinking process of exploring
multiple solutions. CPD could allow designers to explore different, evident-
based paths, providing a way to explore possible solutions. Donating means
distributing the results publicly. The generated CPDs could provide a
structure for designers to store and share the design solutions they have
explored, and refer to the underlying factors (e.g., mediators, moderators,
and mechanisms) that have contributed to those ideas.

####  2.3.1. AI-assisted creativity support tools

Thanks to the ability of AI to model data and extract knowledge (Hwang, 2022;
Kun et al., 2019), creativity support tools have explored the use of AI in
their design. One line of research is on using machine learning models fine-
tuned to a specific domain to support a specific type of design and ideation
task (e.g., (Wan and Lu, 2023; Bunian et al., 2021; Clark et al., 2018; Feng
et al., 2022; Lu et al., 2024; Wang et al., 2010)). For example, VINS uses an
attention-based neural network to retrieve related UI examples from a given
collection to support the brainstorming process (Bunian et al., 2021), and
recent work by Wan et al. (Wan and Lu, 2023) used GAN (generative adversarial
network) trained with relevant research data to support visual brainstorming
and help designers explore the semantic space of diagramming ideation. Despite
the efficacy of such approaches, however, they are limited in that the set of
evidence or resources these AI-assisted CSTs could draw on is quite limited.
As most machine learning systems like these CSTs are trained specifically for
one domain, they cannot easily generalize to other contexts. In addition, the
evidence they use is often restricted in number or the range of content, as
the sets were either hand-curated or drawn from a specific online resource.

Recently, the advance of large language models (LLMs) has opened up new
opportunities to address this space (Joosten et al., 2024; Lawton et al.,
2023; Jeon et al., 2021; Ko et al., 2023; Gero and Chilton, 2019a; Mirowski et
al., 2023; Yuan et al., 2022; Kim et al., 2023a; Wan and Lu, 2023; Petridis et
al., 2023; York, 2023). Compared to existing, task-specific AI techniques,
LLMs are not constrained by domain or by the set of resources they can use to
generate suggestions. Trained on a large corpus of data (e.g., newspapers,
academic papers, blogs, etc.) with human feedback, LLMs enable users to
leverage their large knowledge base to make suggestions in many domains
(Bommasani et al., 2021). Among various application areas, one promising
application of LLMs is to support users in iterating and improving on existing
ideas, allowing them to explain their ideas in more detail (Gero and Chilton,
2019b; Lawton et al., 2023; Kim et al., 2023b; Hou et al., 2024; Zamfirescu-
Pereira et al., 2023a; Epstein et al., 2022; Almeda et al., 2023; Druga and
Otero, 2023; Di Fede et al., 2022; Yuan et al., 2022). For instance, C2Ideas
uses LLMs to generate iterations of the descriptions of users’ design
intentions, highlighting possible design directions by clarifying users’
inputs (Hou et al., 2024). In our work, we posit that LLMs could possibly
support the creation of CPD by making suggestions that help designers better
articulate their ideas. Another set of work discussed how generative AI could
be used to provide a wide range of suggestions based on the users’ existing
ideas, augmenting creativity (Ko et al., 2023; Petridis et al., 2023; Wan et
al., 2023; Rick et al., 2023; Druga and Otero, 2023). These works also
highlight the potential of LLMs in providing recommendations that motivate
designers to think divergently, increasing the number of design directions
generated (Jeon et al., 2021; Angert et al., 2023; McCaffrey, 2018; Klein et
al., 2020). For instance, AngleKindling uses LLMs to suggest different angles
to interpret a press release, helping journalists iterate on their writings
(Petridis et al., 2023). This demonstrates the potential of LLMs in supporting
creativity, especially by bringing in additional perspectives or related
literature that might be relevant to the design space, encouraging evidence-
based design thinking.

Overall, recent works on LLMs and their applications demonstrate the promising
potential to support creativity. Our work builds on these lines of research to
explore the use of LLMs in supporting the creation of CPDs.

##  3\. Design of the Plugin

To streamline the process of creating and iterating on CPDs and support the
designers throughout the process, we developed a plugin in Miro (Miro, 2023),
a widely used diagramming platform. Miro lets users create diverse visual
elements, such as text boxes, circles, and rectangles on a collaborative
board. It is also often used in the early stages of HCD for brainstorming and
ideation purposes. The simplicity of interactions and design elements within
Miro, coupled with its plugin-friendly architecture, made it an appropriate
platform for deploying and testing our concept–how to support designers in
generating CPDs.

Below we describe the design and implementation details of the key features
embedded in our plugin. Our plugin uses a multi-tab design, where each feature
of the plugin can be used separately and independently. We provide a more
detailed visual walkthrough of each component in Appendix C.

###  3.1. Key Features of the Plugin

Figure 2. Key screens of our plugin. Consisting of five features, the plugin
streamlines the creation and validation of CPD

####  3.1.1. Component

Using this feature, users could easily create the elements of CPDs on the
board: moderator (rectangle), implementation strategy (round rectangle),
precondition (trapezoid), mechanism (rhombus), barrier (octagon), proximal
outcome (circle), and distal outcome (circle). Figure 2 displays the layout of
this feature.

For each item shown in the panel, users can easily drag and drop them onto the
Miro board. Once the user drags and drops an element, the plugin creates an
empty entity complemented by a hyperlinked title that directs users to the
page that contains the definition of the entity. This not only facilitates
quick access to the definition but also streamlines the process of element
creation on the Miro platform, sparing users the hassle of manually having to
locate the shape in the platform. Plus, the plugin shows the definition of the
element in a tooltip when the user hovers their mouse over the element.

####  3.1.2. Wizard

To streamline the creation of CPD, we designed and developed a form-based
wizarding tool that guides the user in a step-by-step manner (Figure 2). This
functionality navigates users through the CPD generation process by eliciting
inputs for each component. We guide the creation using backward mapping
(Elmore, 1979), where participants focus on the distal outcome first, then
work backward to the barrier, proximal outcome, strategy, and mechanism. For
each component, the user is provided with an explanation of the component
(e.g., Barrier - ”What is the obstacle that is getting in the way of achieving
the desired outcome?”).

One of the challenges with the creation of CPD may be a lack of knowledge
about relevant theory. To overcome this challenge, we sought to provide
relevant component recommendations to users. In the long run, our plan is to
establish a CPD repository and to recommend factors from our repository.
However, until such a repository is well populated, we sought an innovative
use of LLM to provide recommendations. We hypothesized that LLMs, building on
a large body of data, may be able to provide a set of contextually relevant
factors given other already inputted components (Wu et al., 2023; Hou et al.,
2023).

Thus, we designed the Wizard such that starting from the desired distal
outcome, after entering the content of each component, the system recommends
to the user up to five candidates for each of the following components by
leveraging an LLM. To implement this AI-generated recommendation, we prompted
the LLM with careful instructions. The following is an example prompt for
generating recommendations for a proximal outcome based on users’ input of
distal outcome. The structure of the prompts is similar throughout the step-
by-step process.

> Based on the {previous element(s)} the user have input, recommend 5 possible
> {current element}:
>  \- {the first previous element}: {content for the first previous element}
>  \- {the second previous element}: {content for the second previous element}
>  ...

Once the user finalizes all components, users can easily drag & drop to a
specific area on the board, which creates a complete CPD populated with their
inputs on a designated position.

####  3.1.3. Brainstorming

Our plugin also provides users with the ability to explore potential
candidates for a specific component (Figure 2) using LLM. This is designed to
help support the branching from a specific component in CPDs. For instance,
knowing what the barrier is in a CPD, users may be interested in exploring
different possible mechanisms to address that barrier, expanding their
innovative thinking.

To use this feature, users need to select the specific component they intend
to brainstorm first. Then the plugin proceeds to request information regarding
the contents of the preceding and/or following components related to the
chosen element. With these contextual inputs, the plugin generates and
recommends candidates for the component.

Similar to the approach we took for implementing wizarding, we instructed an
LLM with the following prompt:

> Based on the {preceding element} and {following element} the user has input,
> recommend 5 possible {element that the user wishes to brainstorm}:
>  \- {preceding element}: {content for the preceding element}
>  \- {following element}: {content for the following element}

####  3.1.4. Checking

In contrast to the previous features that primarily aim to initially create or
brainstorm CPD components, the checking feature is designed to help users
verify the CPDs they have already generated (Figure 2). Specifically, our
checking feature lets users diagnose if any of the following issues are
present in their CPD:

  * •

One or more required elements are missing

  * •

One or more elements are not connected to the CPD pathway

  * •

One or more elements are connected in the wrong order

  * •

The CPD does not start / end with implementation strategy / distal outcome

This feature mainly focuses on checking the basic correctness of a CPD. To
start, users should select a generated CPD on the board, including its
components and connections between components. Then, users should click on the
“check” button to perform checking. If the selected CPD does not contain any
of the listed issues, we provide positive confirmation such as “No syntax
issues with your CPD pathway!” If the selected CPD contains one or more of
these issues, we provide suggestive feedback about the specific problems, such
as “You might consider adding the following components.”

####  3.1.5. Help / Glossary

Finally, the help feature allows users to easily access the definition of an
element that is already on the board (Figure 2). By selecting an existing
element on the board and clicking on the “learn more” button, the plugin
promptly displays the element’s definition (i.e., strategy - “Strategy is an
element that the diagram is intended to unpack. It is important to make the
strategy concrete, to write it as it would be performed in that particular
setting.”)

###  3.2. Implementation

Our system was built on a Javascript-based framework (SvelteKit), and deployed
on the Miro SDK 2.0, which allowed the system to interact with the Miro board
(e.g., add/detect items on the board).

To generate every LLM-generated output, we used GPT-4 with the following
parameters: temperature: 1, max_tokens: 256, top_p: 1, frequency_penalty: 0,
and presence_penalty: 0. To avoid potential hallucination issues which are
frequently experienced by LLMs, we provided the model with the definition of
the elements of CPDs, by prepending the definitions to the prompt, whenever
the model is invoked. We used GPT-4 without further tuning because existing
research showed that a generic model is already able to achieve good
performance in domain-specific tasks (Bubeck et al., 2023) and that GPT-4 can
provide domain-specific suggestions with strategized prompting (Nori et al.,
2023).

##  4\. User Study

To understand the use of CPD and our plugin to support human-centered design,
we conducted a user study with design practitioners.

###  4.1. Procedure

Figure 3. An example design sprint

The study was a one-hour, within-subjects online study. First, we provided
participants with an introduction to CPD, explaining its different components,
and gave a tutorial on the process of generating a CPD using an example. We
then asked participants to complete two 10-minute design sprints. For each
design sprint, participants were given a design prompt, a user persona, and a
scenario explaining the issues the persona was encountering in that context.
Figure 3 shows an example design sprint. Participants were asked to generate
CPDs to ideate possible solutions. We structured our study as design sprints
as they are commonly used design methods during early-stage design to explore
design ideas (Banfield et al., 2015) and have been used in prior work to
support the ideation of theory-driven designs (Colusso et al., 2018).

For one of the design sprints, participants were given the plugin to help
generate CPDs. In the other design sprint, they were not given the plugin. We
randomized the order in which a participant would use the plugin, as well as
the order of the design sprints to account for any ordering effects. After
each design sprint, we asked participants questions about their experiences
with using CPD (and the plugin when in the plugin condition). Details of these
questions are shared in subsection 4.4. After the design sprints, we
interviewed participants to further understand their experiences. A detailed
version of the protocol can be found in Appendix B.

###  4.2. Recruitment & Participants

We recruited participants by posting a screener survey on social media. Of
those who expressed interest, we were able to recruit 20 people to participate
in our study. The study lasted an hour. All participants except one completed
both design sprints. One participant had to leave early due to personal
reasons and only completed one of the design sprints (one without the plugin).
We provided a compensation of $50 gift card in compensation for an hour of the
participants’ time. 13 of our participants are professional UX designers,
while others are enrolled in design-related programs. 15 of the participants
have at least 2 years of design experience, and all participants had no
knowledge of causal pathway diagrams prior to the study. Detailed demographic
information of the participants is shared in Appendix A.

###  4.3. Qualitative Analysis

Each study session was recorded and transcribed using Zoom. We used thematic
analysis on the transcripts. One researcher first used thematic analysis on
five transcripts. Then four researchers discussed the excerpts extracted until
they decided on a final codebook. The final codebook includes themes such as
CPD facilitates brainstorming in the early stages of design, CPD helps
establish a common language between designers, and AI recommendations help
paraphrase ideas. The first author then used the codebook to code all 20
transcripts. Throughout the coding process, the researchers mainly focused on
how designers used CPD in human-centered design, and how they used the plugin
to generate CPDs.

###  4.4. Measures & Statistical Analysis

After each design sprint, we asked participants to rate their experience on a
Likert scale of 1-7, exploring whether having access to the plug-in influenced
their experience generating CPDs. Specifically, we asked them to rate “how
hard/easy it was to design a CPD”, “how hard/easy it was to create each
component of CPD graphically”, and “how hard/easy it was to brainstorm the
content of each component of CPD.” We also asked participants to rate their
confidence in the structural correctness (all components are connected and
ordered in the right way) and content usefulness (the likelihood of using the
generated solution in the next stage of HCD) of the CPD generated. We analyzed
these measures using paired sample t-tests to show how the use of the plugin
influenced people’s experience using CPD for design.

Apart from the self-reported ratings, we also measured the amount of time
participants spent on each design sprint and the number of CPD pathways
designers generated for that sprint. We similarly performed paired sample
t-tests to analyze the results.

##  5\. Results

We focused our analysis on two aspects. First, we identified how the use of
CPD facilitated evidence-based HCD. Secondly, we analyzed how our AI-assisted
plugin helped UX practitioners more easily work with CPDs, identifying its
potential to increase creativity and provide evidence-based support.

###  5.1. RQ1: Use of CPD in Human-Centered Design

####  5.1.1. Established an effective and guided design process

Overall, participants found that utilizing CPD helped them design more
effectively for the design sprint challenges. The use of CPD helped direct
participants to think more about the constraints involved in the design, as
well as the outcome of the design process.

##### Identify relevant constraints upfront

Recall that creating CPDs involves mapping out factors of relevance and their
relationships (e.g.“what is the distal outcome”, and “what is the barrier that
may occur given this outcome”). Participants pointed out that when they
performed this mapping, they had to think about the relevant constraints
upfront. Thinking through all the possible barriers that may prevent
strategies from achieving the outcome is a “realistic and down-to-the-ground
strategy” (P7), and helps save time in the design process. For instance, P12
articulated that “instead of thinking about the happy and perfect best case
user flow, it is more important to know the constraints.” P5 also pointed out
that “laying out the barriers, setting up the constraints very very quickly”
immediately highlights the pain point, so that “addressing these barriers
would make the design goals quite tangible when thinking implementation
strategies.” The close attention to the constraints behind a design prompt
helps designers quickly break down the task at hand, and helps them focus
their effort.

##### Highlight the desired goal/outcome of the design

Additionally, participants appreciated the goal-oriented process, rather than
free-form or solution-oriented as most existing design processes are (e.g.,
user journey map, whiteboarding, storyboarding, etc.). In many design
sessions, practitioners may use whiteboarding to “simply throw out ideas, then
sort out the details, whether an idea is a barrier or a mechanism at a later
time.” (P7) Or they may use storyboarding, which starts at the beginning, and
work from the solution to “play out how the solution impacts the outcome.”
(P18)

In contrast, CPD starts from “the high-level vision of why focus on this
particular problem, the distal outcome,” (P6) and “not just jumping into
solutions.” (P2) The generation of a CPD emphasizes the answer to the question
of “what is the long-term goal” and not to “what are some ways to solve this
prompt” (P17). Starting the brainstorming process from these questions, the
outcome of the design, is actually “a natural way of thinking, especially when
tackling complex problems.” (P11) P16 pointed out that one needs to know “the
root cause of the problem” as well as “the impact involved in resolving the
issue” before “identifying the most effective way to address it.” (P16) Thus,
CPD is effective in facilitating goal-oriented design because it offers “a
direct connection between everything on the users’ side to the larger context
side, from a visual standpoint.” (P6) Furthermore, CPD’s emphasis on the
outcome helps designers pay attention to“the realistic and practical goal of
the design,” (P15) and “addressing the most fundamental issue in the design
context,” (P7) instead of thinking about “designing prettier solutions.” (P20)

####  5.1.2. Ideation

When participants were introduced to the concept of CPD, they were intrigued
by its potential as a tool for ideation. This is because CPD diagrams can
expand into branches, and thus help “lay out all of the possible solutions”
(P5) and “generate a breadth of ideas” (P14). Multiple barriers could emerge,
each having numerous potential mechanisms and strategies. During the
brainstorming sessions, the CPD “turned into a huge diagram with a bunch of
possibilities, moving in so many diverse directions.” (P7) Additionally, even
after designers have identified a set of barriers and have started working on
the design solutions, it is easy to “backtrack and simply extend another
branch” (P18) if another barrier pops into their mind.

As designers expand the CPDs in multiple branches, a key step is to think
about the causal connections between components. These causal connections kept
designers’ attention span contained throughout the ideation process. As P19
said, “creative people are either hyper-focused or are not motivated. But CPD
guides you through the brainstorming by asking you to connect from one
component to another.” (P19) Similarly, P6 pointed out that “the set of
questions highlights the connection between the component I’m working on and
the other ones I’ve created, which kept me on track, dragging me back to the
problem space.”

However, some participants found that focusing on how the components should be
connected to one another at a high level distracted them from brainstorming
the individual elements. Specifically, they found that their attention was
split between “ideating additional possibilities of an individual element” and
“following the step-by-step process to brainstorm the next component.” (P19)
P15 found herself “moving between the list of questions of the CPD process,
jotting down the barrier, but then moving to list out strategies and
connecting them to mechanisms.” Essentially, participants knew that they
needed to implement both individual elements and the causal connections
eventually. So they tried to multitask but found “the thoughts scattered
across.” (P2) However, participants also pointed out this issue could be
addressed by handling elements and the connections separately. P9 said that
she would “focus on individual elements first,” then “come back to the
connections in a second design session.” This process would ensure that one
can focus their thoughts on either just component content or just high-level
causal relationships.

####  5.1.3. Strategic prioritization

CPDs emphasize the connections between the components, which helps designers
play out how a design solution addresses the design prompt, and assist in
strategic evaluation. The directional relationships represented by causal
connections in CPDs “served as a guardrail to keep track of why a component is
created, how each component is addressing this design scenario.” (P14) The
“simple but powerful” (P14) causal connections between components make it
“easy to trace back through each pathway and figure out how a solution or a
strategy plays out to its mechanisms, resolves the barriers, and achieves its
final outcome.” (P20)

In addition, since CPD is able to demonstrate how each solution addresses the
design problem, participants also used it to compare and prioritize different
solutions. Designers shared that CPD is able to present various solutions on
multiple branches without becoming too complex. Even as a CPD expands into a
large tree with multiple pathways, its main structure retains a “simple and
straightforward” nature due to the interconnection of components through
causal relationships (P11). In addition, the different branches helped
designers see “how the different design solutions are influenced by various
barriers,” which is useful for them to then “evaluate the likelihood of each
barrier, the significance of that barrier, and determine overall which
corresponding solution to actually implement and prototype.” (P20)

####  5.1.4. Facilitating communication

The use of CPD can also help facilitate communication with stakeholders. P10
pointed out that CPD helps establish a common language among designers in a
team setting: “every designer has their own language, and I’ve noticed how
common it is to have miscommunication issues with others about an idea in
brainstorming sessions.” P20 also said that having a “clear and
straightforward” process that everyone understands could “align the way we
[designers] see each others’ ideas, banding to the same wavelength.”
Furthermore, “reducing the cost of communication” means that designers could
“focus more on the ideas themselves and not how to present them.” (P5)

Additionally, participants expressed that CPD can bridge the communication gap
between designers and the executive team. As discussed before, the process of
CPD underscores the goal of the design, which aligns with how product managers
and sales consultants think about “strategizing a product and conducting
business-level market research.” (P16) P9 also described that “the distal
outcome in CPD” corresponds exactly to product managers’ viewpoint of “why
this problem matters” and “what is the long-term business goal of the
product.” Such correspondence means that CPD could easily and effectively
communicate designers’ ideas to product managers. Furthermore, CPD offers “the
visual clarity for people to easily trace through the logic” due to its
simplistic structure and causal connections (P11). Such a benefit lowers the
barrier of communication between designers and product managers.

####  5.1.5. Concerns about potential misuse

Because the CPD process was positively perceived to help with ideation and
strategic evaluation, participants expressed concerns that it may be used as
“a tool to oversimplify complex conversations without any proof.” (P8) As P20
further explains, “I’m concerned people would just use this without research,
and that could mean putting in random things and coming up with solutions that
don’t work.” Instead, CPD should be used as “a guided process that helps
organize the results of user research.” (P17) P7 summarized this sentiment
with “A useful tool such as this would only be effective when in the right
hands. Simplifying and lowering the barrier to brainstorming is certainly
great. But we also need responsible designers who do their due diligence, and
do the right user research, before using the CPD to lay out their thoughts.”
(P7)

###  5.2. RQ2: Use of Plugin in Generating CPDs

####  5.2.1. Quantitative results

Figure 4. Quantitative results of our user study. Bars indicate standard
errors (*: $p<.05$, **: $p<.01$)

Our analysis of the participants’ self-reported ratings showed that they had a
positive attitude toward the plugin. As shown in Figure 4, their self-reported
rating of the ease of creating each component ($t(18)=-2.370$, $p<.05$) and
ease of using the CPD process to design ($t(18)=-2.987$, $p<.01$) increased
when they had access to the plugin. Additionally, the plugin increased
participants’ rating of the easiness of brainstorming the content of each
component ($t(18)=-2.649$, $p<.05$).

In addition to the self-reported perception towards the use of our plugin,
participants’ self-rated confidence level of the generated CPD’s correctness
($t(17)=-3.409$, $p<.01$) and usefulness ($t(17)=-3.692$, $p<.01$) increased
when they had access to the plugin (Figure 4).

When analyzing the CPDs generated, we also found that when using the tool,
participants created more pathways in their diagrams
($M_{{without\\_plugin}}=1.47$ vs. $M_{{with\\_plugin}}=2.32$; $p<.05$), using
about the same or less time ($M_{{without\\_plugin}}=603(s)$ vs.
$M_{{with\\_plugin}}=556(s)$; $p>0.5$). For examples of CPDs generated without
and with the tool, see 5(b) and 5(a).

Overall, our quantitative analyses demonstrated the plugin helped designers
more easily navigate the use of CPD, helped them brainstorm more
possibilities, and increased their confidence in the work produced.

(a) A CPD generated by P13 under the condition without access to the tool

(b) A CPD generated by P8 under the condition with access to the tool

Figure 5. Sample CPDs generated during the design sprints by participants to
address the prompt shown in Figure 3

####  5.2.2. Alleviated cognitive workload

Generating CPD involves remembering the framework itself, which could distract
designers from attacking the problem. The plugin provided the framework
details in an easily consumable way and guided users through the process of
creating a CPD in an orderly fashion, allowing participants to focus on the
design task. By reducing the cognitive workload involved, the plugin not only
expedited participants’ design process but also increased their confidence in
the work they produced.

When participants did the design sprints without any additional help, they
expressed that it was particularly difficult to remember the association
between each component and its corresponding shapes, such as “remembering if
the shape for the barrier is a diamond or an octagon.” (P7) They also
struggled with the naming of the components, pointing out that “looking at the
term ‘mechanism’ does not inform much about its meaning and functionality.”
(P17) Thinking about the CPD framework distracted designers from the ideation
process, as they felt like their brain was “split in half with one focusing on
building shapes, the other on the design.” (P16) This burden of multitasking
was alleviated by using the Components feature of the plugin. With an easy
drag-and-drop feature where users can generate each component without thinking
about its corresponding shape, the tool greatly “expedites the time” (P6)
because participants were “less caught up by the framework itself to actually
think about the problem.” (P5)

Participants also struggled with how CPD components should be interconnected,
including “what follows after a mechanism,” or “whether it is a barrier or
strategy that should come up next.” (P3) They constantly referred to the
generation process before diving into the design details. But the step-by-step
process of the Wizard feature helped participants “focus attention on the
answer for each component and not worry about the order of them.” (P3) P19
also testified that “bypassing thinking about the ordering made me more
empowered to keep searching for a clearer picture of what the design should
be.” Since participants were able to “think through each component without the
disturbance of the shapes or how they should be ordered,” using the plugin,
they felt “more sure and more confident” (P13) of their design. Similarly, P9
said that “using the Wizard feature, I knew that I followed the right
procedure,” and “I had more confidence in my design because I spent all my
energy on it.”

####  5.2.3. Increased creativity with the support of AI-generated
recommendations

Utilizing LLM, our plugin offered suggestions of component content when
prompted in the Wizard and the Brainstorming features. Details of the prompts
used were discussed in section 3. Generally, participants expressed positive
attitudes toward using AI-generated content in designing CPD helped them
clarify their ideas and sparked innovative thinking during brainstorming.
However, they also expressed concerns about blindly using the suggested
content.

##### Increased creativity in brainstorming sessions

As discussed in our quantitative findings, the use of the plugin increased the
number of pathways designers were able to generate. Participants’ comments
corroborated this observation and highlighted that some AI-generated content
helped increase their creativity. For example, when given the same design
sprints (Figure 5), P13 (without tool) only generated one pathway, while P8
(with tool) not only generated a similar path as P13, but also generated a
distinct second path. Specifically, P8 commented that the brainstorming
feature “prompted me to think about a new direction as I was thinking about
what barriers there are in the problem space.” And P8 described that he would
not have thought about “the idea of running an ad campaign in a short
timeframe if I were just throwing out ideas by myself. But seeing the
suggestions by the tool immediately clicked for me. So I also explored this
path in the CPD.” Further, of the path that P13 and P8 generated that were
similar, P8 also was able to consider a different mechanism. These differences
would have allowed P8 a richer design space (that is still evidence-based) to
explore. P9 shared a similar perspective: “having the AI on the side was like
having lots of teammates with really different viewpoints.” Additionally, AI
can be used to expand creativity by using it to weed out the wrong directions:
“In any design, your first 50 ideas are going to be trashed, but you have to
get them out to get to idea 51, which is how the AI can help.” (P14)

##### LLM helped with the articulation of ideas

AI did not always generate new ideas that the participants had not already
thought of. When designers’ ideas overlapped with AI recommendations,
designers found that how AI phrased these ideas was helpful. Participants
noted that AI articulates these ideas in “effective, condensed phrasings,” or
the AI “uses expressive terminologies (i.e. route optimization algorithms) to
summarize” (P3). Reading the AI suggestions helped designers “build the idea
firmly in the mind in a clear and direct way.” (P14) AI’s articulation and
clarification of the concepts helped participants focus more of their energy
on the “meaning and impact of the ideas” (P14), rather than “drilling on how
to best describe them.” (P20). For example, when communicating the same
barrier in their CPD (5(a) & 5(b)), P13 (without tool) had to use a full
sentence to describe the barrier: “people are concerned that the metro does
not follow the schedule” whereas P8 (using the tool) was able to describe it
more succinctly “concerns about inconsistent schedule.”

##### Desire for more context-specific/relevant support

Though participants were generally positive about AI recommendations, they did
point out that some of the AI recommendations were too generic and unhelpful,
especially when users did not provide sufficient context information in
existing components. For example, when P17 inputted (“improve bus schedule”)
as a generic distal outcome in the Wizard, the plugin was not able to create
specific and contextualized examples of barriers. Instead, the generated
suggestions were “oversimplistic and did not seem to fit the problem context.”
(P17) P14 had a similar issue, where the AI provided out-of-context
suggestions when it was given non-descriptive component content to work with.
Additionally, P6 said that in his previous encounters with AI recommendations,
it is common for the AI to get repetitive, and it had the tendency to
constantly resort to  “recommending designing an immersive VR experience” for
any given design problem when he did not define it with excruciating detail.

##### Making recommendations (more) evidence-based

Another challenge with using the AI recommendations is the lack of information
about where the recommendations came from. Participants discussed the
importance of determining whether the suggestions actually address the
specific problem space. Making that determination requires that the designers
“understand what is the background of this specific concept recommended.”
(P12) For example, in our study, participants were careful about using AI-
generated content and only chose to proceed if they had previously studied the
concept or knew of its background research. But very often, designers do not
know everything about the AI recommendations (e.g., “what is the meaning of
the suggestions” (P2), “why is it being recommended” (P19), etc.).
Participants expressed concerns about what designers may do in these
situations. P3 highlighted that “not knowing the research behind these
recommendations would sow doubt in my mind about my design, which is not a
good sign.” P2 also emphasized that there could be significant consequences in
product deployment if irresponsible designers just choose to “click through AI
suggestions in Wizard without judging whether it actually fits the problem.”
Participants noted that this could be addressed in the future by providing
more context information for each recommendation. For instance, seeing
“external links and research papers to explain the reasoning behind” (P18)
would help them more easily understand and contextualize the AI
recommendations. Plus, as the participants pointed out, seeing “the background
research through external sources” (P20) would also validate that the AI is
not hallucinating, addressing another common concern that participants shared
when they were unfamiliar with the AI-generated content.

##  6\. Discussion

In this work, we explored how CPD—originating in implementation science—may be
used to support theory-driven design in the domain of HCD. To facilitate its
use, we developed a Miro plugin with several CPD guidance features and infused
it with AI recommendations powered by an LLM. We then conducted a user study
with practitioners, where we found that CPD may be helpful in supporting
divergent and convergent work within the early stages of design and
demonstrated that our plugin is helpful in reducing overhead cognitive costs
and helping guide CPD development. Modern design practice is not purely
idiosyncratic as Schön described (Schon, 1983), and CPD shows promise in
making both divergent and convergent processes more predictable. Below we
discuss our interpretations of the study findings in more detail.

First, our work demonstrated that the use of CPD facilitated both divergent
and convergent thinking through an evidence-based lens. During the divergent
processes of addressing the design sprint (e.g. ideation), CPD allowed
practitioners to easily diverge and expand their innovative thinking. In an
ideation context, this can support the create activity as noted in
Shneiderman’s framework (Shneiderman, 2002). With the use of visual elements,
designers found it easy to organize their ideas and branch off to explore new
factors and new paths. Additionally, backward mapping (Elmore, 1979) enabled
designers to start with the desired distal outcome and work backward to
explore relevant factors in a step-by-step manner, specifying the causal,
moderating, or mediating relationships. CPD helped designers reflect on
relevant evidence related to the design problems, reflecting the support of
the collecting activity (Shneiderman, 2002). Instead of impeding creativity,
these “guardrails” helped remind designers of the design objective and
provided a clear structure for evidence-based thinking through the mapping of
outcomes, barriers, mechanisms, and strategies.

CPD can also support convergent processes in HCD. During our sprints, CPD
helped designers with strategic prioritization, converging their ideas to
select a solution for the next stage of HCD. After initial brainstorming, the
generated CPD may have multiple branches, each suggesting potential solutions.
To determine which ones to prototype, designers were able to examine across
branches and contrast the effectiveness of each solution. Using the identified
goals and constraints, they were able to strategically select the ones they
believed to have more influence on the problem context and prioritize the
implementation of that solution. In practice, we would expect designers to
make these judgements by referring back to relevant user research and
optimizing on the paths given their contexts.

Our study also uncovered CPD’s potential as a communication tool. Within an
ideation context, this is relevant for both the relating and donating
activities (Shneiderman, 2002), but this type of communication support is also
critical throughout HCD. Since each designer often has their own language to
describe a design idea and articulate how to approach that idea, it becomes
difficult for them to brainstorm together in a team setting. As explained in
(Gonen, 2020), design professionals’ drawing practices serve to communicate
their ideas instead of illustrating designs accurately. In our study, we
observed how CPD supported such a process of visual representation of complex
ideas. CPD could help provide an established and easy-to-understand framework.
For instance, CPD can guide a team of designers together through the process,
starting from the outcome of the design, to barriers, and then to strategy.
Throughout the process, CPD’s procedure ensures that team members are
brainstorming the same component. This suggests a promising future research
direction–exploring the use of CPD during team ideation (Wang and Nickerson,
2017; Gabriel et al., 2016; Frich et al., 2019) and iteration (Little et al.,
2010; Kim et al., 2017; Yu and Nickerson, 2011). Additionally, designers
highlighted the potential of CPD as a tool to present their ideas to business
stakeholders and product managers. They found that the questions a CPD is
asking coincide with the questions the executive teams ask them. Future work
could further investigate how product managers respond to CPD and how
effective CPD may be in facilitating meetings between designers and product
managers.

Further, based on our observations, it seems that CPD may also help support
designers (and user researchers) to build and communicate their own theories
more effectively. In this sense, CPD can support—as been noted in prior work
(Colusso et al., 2019; Beck and Ekbia, 2018; Redstrom, 2017)—the theorizing,
which is often done by practitioners in practice. Practice is a type of
theorizing (Redstrom, 2017), and different concepts and relationships are
uncovered and tested in practice and can be useful if bubble up to research
(Gray et al., 2014). This makes CPD more than just a tool to help solve a
design problem, but also supports the testing of novel theories from the
bottom-up and is in itself a knowledge contribution (Beck and Stolterman,
2016).

In addition to studying CPD’s potential use in HCD, our research also explores
the use of a dedicated tool to support CPD usage. We found that our plugin
helped designers learn and apply CPD. In particular, the Components feature
helped them easily generate components without thinking about which shape to
use. The step-by-step CPD building Wizard feature also guided them through the
process without being concerned about how the components should be linked.
Participants reported higher perceived ease of use of CPD with our tool. By
reducing their cognitive workload, the tool helped designers spend more of
their energy on the design itself, and participants reported feeling more
confident in the accuracy and usefulness of their CPDs created. One thing to
note, however, is that while backward mapping (Elmore, 1979) through CPD
provides a logical way of thinking about the design problem, participants had
trouble navigating this process and brainstorming individual elements at the
same time. Designers found themselves multitasking to brainstorm both
individual components and causal connections between components at the same
time. This may be addressed by increasing flexibility in our step-by-step
wizard tool and allowing users to develop different parts of the causal
pathway first.

Finally, an interesting and important point of potential misuse of CPD in HCD
was raised by participants. Part of that perception may stem from general
concerns that tools (especially AI-based tools) are making the design too
easy—and that may be destroying design (Matthews et al., 2023; Altavilla and
Blanco, 2020). There are two things to address. First, we envision CPD to
complement design, and not replace. Having good causal pathways alone does not
guarantee good designs. Skilled designers are still needed to perform that
work. Second, CPD is meant to build on existing user research. As highlighted
by existing research (Kun et al., 2019; Wan and Lu, 2023; Petridis et al.,
2023), it is essential to include evidence throughout the design process. The
fact that participants expressed concerns that CPD may be misused without
proper user research may be both an artifact of our study design (sprints
where we gave designers pre-developed personas and scenarios), and an
indication that our Miro plugin may be lacking in this respect though we had
intended to express motivation for evidence-based design. We need to more
effectively prompt and remind designers to incorporate evidence-based insights
when building their CPD. Thus, there is also a rich opportunity to explore
ways to seamlessly integrate user research outputs into CPD usage so that
designers are interfaced with more domain-relevant suggestions.

###  6.1. Supporting Ideation with AI-Assisted Tools

Overall, our study revealed the potential of adopting LLMs to support
ideation, specifically, in brainstorming relevant components in causal
pathways. Recent research in HCI has begun to integrate LLMs to help users’
ideation processes within various contexts, such as news article writing
(Petridis et al., 2023), idea machine (Di Fede et al., 2022), and creative &
argumentative writing (Lee et al., 2022). Since LLMs have been trained on vast
amounts of information, it is possible that they could simulate human
cognition and help offer more relevant suggestions based on the world’s
collective knowledge (Schmidt et al., 2024). Our work builds on this body of
literature and provides both quantitative and qualitative insights into how
LLMs can support ideation.

Quantitatively, our work showed that, with LLM assistance, participants were
able to generate and explore more variations of causal pathways.
Qualitatively, our participants noted that AI assistance was able to offer
suggestions from different viewpoints, similar to working with colleagues. To
them, the AI recommendations not only made sense, but were also constructive
and diverse, which expanded their perspectives on the problem. This reinforced
prior works’ discussion of how LLMs could support creativity activities by
making diverse recommendations and stimulating innovative ideas (Ko et al.,
2023; Petridis et al., 2023; Wan et al., 2023; Rick et al., 2023; Druga and
Otero, 2023; Jeon et al., 2021; Angert et al., 2023; McCaffrey, 2018; Klein et
al., 2020). We also found that LLMs helped rephrase and reframe ideas in a
more direct and effective way when AI’s recommendations overlap with
designers’ ideas. In particular, LLMs articulated concepts by drawing on
succinct terminologies from their large knowledge database. These
terminologies helped clarify designers’ thoughts on a conceptual level, which
reduced their mental workload on rephrasing and reframing the idea themselves.
his finding echos prior research’s discussion of how LLMs could help explain
users’ ideas in a clearer way to facilitate brainstorming (Gero and Chilton,
2019b; Lawton et al., 2023; Yuan et al., 2022; Kim et al., 2023b; Hou et al.,
2024; Zamfirescu-Pereira et al., 2023a; Epstein et al., 2022; Almeda et al.,
2023; Druga and Otero, 2023; Di Fede et al., 2022).

Another benefit of using LLMs is that it may be able to help reduce design
fixation in the ideation process. Design fixation refers to designers’
convergence on one idea over divergently thinking about multiple solutions
(Youmans and Arciszewski, 2014). Prior works (Jeon et al., 2021; Figoli et
al., 2022; McCaffrey, 2018; Klein et al., 2020) have discussed that the use of
LLMs increased the number of ideas designers could generate compared to
without use, indicating an increase in creativity and decrease in design
fixation. As suggested in Section 5.2.3, we observed a similar phenomenon in
our finding. Our tool facilitated the generation of additional ideas for CPD,
which resulted in more and richer causal paths for them to explore.

Given these affordances, our AI-assisted plugin was well received by the
participants and participants were positive about integrating this type of AI-
generated recommendations in their future work. However, participants noted at
times the suggested factors were too generic and not relevant enough,
suggesting that our tool should be improved to provide more context-specific
recommendations. We noted that this primarily occurred when the information
provided by participants was too high level to begin with, which resulted in
the LLM responding with generic factors that may not be specific to the
problem space (Zamfirescu-Pereira et al., 2023b; White et al., 2023). Future
work should explore how to guide users to generate specific and informative
outputs. Specifically, the tool could give users a tutorial on how to best
prompt LLMs, or present the users with several examples with the expected
level of specificity.

Despite the usefulness of our AI-assisted tool in supporting ideation, our
participants did express concerns about generative AI’s hallucination problem
(Alkaissi and McFarlane, 2023). Participants were sometimes hesitant to use
the recommendations because they wanted to know why AI was making that
suggestion, especially when designers had no background knowledge about the
AI-recommended content. This echoes prior research’s findings that AI-driven
tools should provide suggestions and generated content with support from data
to enhance its credibility (Liao et al., 2020; Mohseni et al., 2021; Kun et
al., 2019). This is a critical issue given that CPD is meant to be evidence-
based and theory-driven. Our hope is that in the long run, we can build up a
repository of developed and experimentally evaluated causal pathways to power
our recommendation to designers. However, because such a repository is yet to
exist and even if it exists, may be sparsely populated, we envision there is
still value in utilizing LLMs in the process. One possibility is to produce
intermediate reasoning steps by leveraging the chain-of-thoughts (Wei et al.,
2022) technique, which may not only serve as an explanation that enables users
to examine how reasoning processes unfold but also enhance the actual accuracy
of the output (Wei et al., 2022). Indeed, recent research showed that
strategized prompting can allow generic models to achieve good performance in
domain-specific tasks (Bubeck et al., 2023). Another is to have the LLM
provide recommendations on a more constrained body of knowledge (e.g., ask it
to make recommendations from an inputted set of papers or factors). However,
even if the information is accurate, it would be important to ensure users’
trust in the system. The challenge then becomes more about providing
information about where the recommended concepts come from (information
provenance), which can both help improve trust and help designers make more
informed decisions.

While our study mainly focused on testing a digital AI-assisted tool in
virtual/remote sessions, our tool may also be used to support in-person design
sessions. We envision that a facilitator could project our tool on screen and
ask participants to discuss and collaborate around it. And whenever needed,
participants can leverage the benefits of tangibility in the physical spaces
(Yaneva, 2005; Frens and Hengeveld, 2013; Jowers et al., 2008; Gulay and
Lucero, 2019). For instance, working on top of the projected screen showing an
initial version of the CPD, participants could use Post-Its to brainstorm
additional ideas and lay over the current version. Participants could use
Post-Its to lay out simple sketches to increase people’s understanding of an
idea. They could also use Post-Its to facilitate a voting process for idea
selection. Future work can explore how to best support this process in-person
session to ensure effective design outputs.

##  7\. Limitation & Future Work

The design sprints we used for our work have both strengths and weaknesses.
Although the sprints allowed us to study the use of CPD for HCD in a more
controlled setting, these design sprints are stylized design tasks that are
often used in the early stages of design. They did not allow us to examine the
use of CPD to support design in-situ, and across later stages of design.
Additional research is needed to further explore how CPD and our plugin can be
integrated into the existing design process and be used to support different
phases of design.

##  8\. Conclusion

This paper explores the potential synergy between Causal Pathway Diagrams
(CPD) and Human-Centered Design (HCD). While CPD traditionally serves as a
powerful tool for theory-driven behavioral implementation strategies, our
investigation has demonstrated its applicability and benefits in the early
phases of HCD. Designers embraced CPD as a means to emphasize goal-oriented
design by addressing root causes, particularly for brainstorming and strategic
prioritization. To address the conceptual and practical challenges inherent in
CPD adoption, we introduced a user-friendly CPD plugin integrated with
generative AI capabilities. This tool helped streamline the CPD creation
process and encouraged evidence-based thinking and creativity among designers.
Our findings shed light on the opportunities and responsibilities associated
with integrating AI assistance into creative, evidence-based design practices,
offering valuable insights for both the HCD and implementation science
communities.

###### Acknowledgements.

Research reported in this publication was supported by the National Cancer
Institute of the National Institutes of Health under Award Number P50CA244432.
The content is solely the responsibility of the authors and does not
necessarily represent the official views of the National Institutes of Health.
We also thank Bryan Weiner, Cara Lewis, Aaron Lyon, and other members of the
OPTICC Center.

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##  Appendix A Demographics

Table 1 shows the demographic information of participants in the study:

Table 1. Demographics information of participants. * indicates the participant only completed one of the design sprints PID |  | Age
---
range
Gender | Occupation |  | Experience
---
with design
P1 | 30-39 |  | Prefer
---
not to say
Graduate student | 2-5 years
P2 | 24-29 | M | Product designer | 2-5 years
P3 | 18-23 | F | Graduate student | 1 year
P4 | 18-23 |  | Gender-
---
non
conforming
Graduate student | 1-2 years
P5 | 24-29 | M | UX designer | ¿ 5 years
P6 | 24-29 | M | UX designer | 2-5 years
P7 | 30-39 | M | UX designer | ¿ 5 years
P8 | 30-39 | M | Graduate student | 2-5 years
P9 | 24-29 | F |  | Pharmaceutical
---
scientist
2-5 years
P10 | 18-23 | M | UX designer | 2-5 years
P11 | 18-23 | F | UX designer | 1-2 years
P12 | 24-29 | F | Graduate student | 2-5 years
P13 | 24-29 | F | Graduate student | 2-5 years
P14 | 24-29 | M | UX designer | ¿ 5 years
P15* | N/A | F | UX designer | 2-5 years
P16 | 24-29 | F | Product designer | 1-2 years
P17 | 24-29 | M | UX designer | 2-5 years
P18 | 30-39 | F | UX designer | 2-5 years
P19 | 24-29 | M | Product Designer | 2-5 years
P20 | 24-29 | F | UX designer | 1-2 years

##  Appendix B User Study Protocol

###  B.1. Phase 1

  * •

Study Introduction

  * •

Introduce CPD with an example

  * •

Background questions

    * –

What are your prior experiences with Miro? What do you usually use it for?

    * –

How much do you know about CPD?

###  B.2. Phase 2

Invite participant to Miro board without plugin

  * •

Provide 10 minute design challenge

  * •

Ask participant to generate a CPD depicting the challenge

  * •

Ask participants to complete the post-task short survey

  * •

Answer the following questions on a scale of 1-7

    * –

How hard/easy was the process to design a CPD?

    * –

How hard/easy was it to create each component (e.g., Strategy, Mechanism,
etc.)?

    * –

How hard/easy was it to brainstorm the content of each component to create the
CPD (e.g., Strategy, Mechanism, etc.)?

    * –

How confident are you about the correctness of the CPD designed?

    * –

How confident are you about the usefulness of the CPD designed?

###  B.3. Phase 3

Invite participants to Miro board with plugin (install the plugin)

  * •

Briefly go over plug-in features

  * •

Provide 10 minute design challenge

  * •

Ask participant to generate a CPD depicting the challenge

  * •

Ask participants to complete the post-task short survey, which is the same one
as used in Phase 2

Note: We would randomize the order of phase 2 and phase 3 for each
participant.

###  B.4. Phase 4. Follow-up interview

  * •

Was it difficult applying CPD into this design problem?

  * •

Was it helpful using CPDs to tackle these design challenges?

  * •

Would you use CPDs in your future design work? Why/Why not?

  * •

Did the Miro Plug-in help or impede your ability to create CPDs? In what ways?

  * •

What did you think about each feature?

  * •

Was it difficult to use? Did it make a difference when you were creating the
CPD?

  * •

Do you envision any changes to the feature?

  * •

Did the Miro Plug-in help or impede your ability to design? In what ways?

  * •

What did you think about each feature?

  * •

Did it make a difference when you were designing?

  * •

Are there any additional features you think might be useful that might help in
creating the CPD?

##  Appendix C Key Screens of Our Plugin

Figure 6. Interaction flow of the ‘Component’ feature. From the (1) plugin
panel, users can (2) drag & drop each component to the board and (3) type the
content inside it.

Figure 7. Interaction flow of the ‘Wizard’ feature. Once the user first (1)
provides their desired implementation outcome, the system (2) guides them
through the other components, along with providing AI-generated
recommendations to help them generate CPDs. Once the user types/selects
contents for every component, the feature prompts the user to drag & drop to
which they want to place their CPD.

Figure 8. Interaction flow of the ‘Brainstorming’ feature. The plugin first
asks the user to provide (1) the component they would like to brainstorm and
(2) the preceding / (3) the following components of it. Then, the feature
prompts the user to drag & drop to the board, which then generates five
recommendations for the component.

Figure 9. Interaction flow of the ‘Checking’ feature. (1) Once the user
selects the CPD that they want to check and clicks the ‘Check’ button on the
panel, (2) the plugin detects and shows any potential issue (if any) Figure
10. Interaction flow of the ‘Help / Glossary’ feature. (1) Once the user
selects the component that they want to learn more about and clicks the ‘Learn
more’ button on the panel, (2) the plugin shows the details of the selected
component.