# From Paper to Card: Transforming Design Implications with Generative AI

Donghoon Shin  0000-0001-9689-7841<EMAIL_ADDRESS>University of
WashingtonSeattleWAUSA ,  Lucy Lu Wang  0000-0001-8752-6635<EMAIL_ADDRESS>University of Washington,
Allen Institute for AISeattleWAUSA and  Gary Hsieh  0000-0002-9460-2568
<EMAIL_ADDRESS>University of WashingtonSeattleWAUSA

(2024)

###### Abstract.

Communicating design implications is common within the HCI community when
publishing academic papers, yet these papers are rarely read and used by
designers. One solution is to use design cards as a form of translational
resource that communicates valuable insights from papers in a more digestible
and accessible format to assist in design processes. However, creating design
cards can be time-consuming, and authors may lack the resources/know-how to
produce cards. Through an iterative design process, we built a system that
helps create design cards from academic papers using an LLM and text-to-image
model. Our evaluation with designers ($N=21$) and authors of selected papers
($N=12$) revealed that designers perceived the design implications from our
design cards as more inspiring and generative, compared to reading original
paper texts, and the authors viewed our system as an effective way of
communicating their design implications. We also propose future enhancements
for AI-generated design cards.

design card, translational science, generative AI, large language model, text-
to-image model

††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: 10.1145/3613904.3642266††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

One of the key goals for research in an applied field such as human-computer
interaction (HCI) is to generate insights that can directly impact practice—in
our case, design practice. Many authors attempt to achieve this through the
design implication section in an academic paper. However, despite best efforts
in communicating these insights, studies have found that design implications,
as presented in academic papers, are rarely consumed and used by design
practitioners (Colusso et al., 2017). Designers describe difficulties reading
the often jargon-filled papers and not finding academic papers useful (Colusso
et al., 2017).

This has led to several calls to develop better translational resources to
bridge the gap between research and practice (Colusso et al., 2017, 2019).
Instead of relying on academic papers to communicate valuable research
findings to design practitioners, what may be needed are other types of
resources to support this translation. One approach is to distill the insights
into design cards. Design cards are tools that facilitate the design process,
and can contain several types of design knowledge—from human insights to
material & domain knowledge (Hsieh et al., 2023). There is a long tradition of
creating design cards to communicate research insights (Alkhuzai and Denisova,
2021; Chung and Liang, 2015).

However, while design cards may be more accessible and provide more
prescriptive insights to practitioners, one challenge with relying on using
design cards to communicate academic findings is how these cards should be
generated. It is unrealistic to expect researchers to do it, as HCI
researchers describe the lack of time and competing responsibilities that
prevent them from doing more outreach work (Williams et al., 2022). They also
lack the proper incentive to do this type of translational work (Colusso et
al., 2019). Further, even if researchers are motivated, they may have limited
understanding of what designers are seeking in design implications, and lack
the skills needed to generate these cards so that they are inspiring (Sas et
al., 2014). A potential solution may be to leverage crowdwork—ideally crowds
of designers—to perform this translational work; this approach has been
explored for the creation of videos for research talks (Vaish et al., 2018).
Nonetheless, changes in incentives are required for long-term sustainability.
Furthermore, reliance on volunteer contributors can lead to spotty coverage
and a biased focus on certain topics over others (Denning et al., 2005).

In this paper, we propose the use of generative AI models to support this
important translational work. Generative AI models, such as large language
models (LLMs) and text-to-image models, have recently shown remarkable
capabilities in producing high-fidelity texts and images, thus being adapted
for various creative tasks (Chung et al., 2022; Shakeri et al., 2021; Ko et
al., 2023). Motivated by this line of research, we believe generative AI
models could provide a scalable and efficient way of translating academic
findings into a prescriptive format such as design cards, without requiring
significant time or effort from researchers and designers.

Figure 1. A design card built on Baughan et al. (2020) (Baughan et al., 2020)
using our system

This figure illustrates a design card built on Baughan et al. (2020) using our
system, which is double-sided. This figure illustrates the front side of the
card on the left side, which consists of the title, description, and image. On
the right side, the figure illustrates the back side of the card, which
consists of a paper summary, evidence, and citation.

To test our hypothesis, we develop and evaluate an end-to-end system to
automatically generate design cards from HCI publications. We first ran a
preliminary interview study with design practitioners ($N=12$) to inform the
design of design cards built on academic papers. We presented participants
with mock-ups of design cards that we created from three HCI papers, each with
several design elements and alternate formats. Based on our findings, we
iterated on our design and built a system that automatically generates a
design card from the design implication text extracted from a paper using LLMs
(i.e., GPT-3 (Brown et al., 2020)) and text-to-image models (i.e., DALL-E 2
(Ramesh et al., 2022)).

To test our system and explore future enhancements of AI-generated design
cards, we evaluated a set of generated cards through a survey study with
designers ($N=21$) and the authors of HCI papers ($N=12$). We found that
designers perceived content from AI-generated design cards as more inspiring
and generative compared to the text of design implications sections, and did
not consider the generated content to be different in validity,
generalizability, or originality. The paper authors further reported that the
contents in a design card were overall clear and accurate, and highlighted the
potential of cards in strengthening the delivery of their contributions in
HCI. In addition to this positive feedback, conversations with designers and
authors also surfaced future directions for enhancing AI-generated design
cards.

Our work offers several important contributions:

  * •

Surfacing design elements of what design practitioners want from design cards
as a form of translational resource

  * •

Implementing a system that helps streamline the generation of design cards
from HCI papers using generative AI models

  * •

Survey results with designers and HCI paper authors demonstrating the
potential added inspirability and generativity of design cards generated by
LLMs, as well as the potential of our system as a delivery tool for authors to
present their contributions

  * •

Insights for the future enhancement of AI-generated design cards

##  2\. Related Work

Generative AI models are powerful tools that have been incorporated into many
creative and practical tasks. Large language models (LLMs) such as GPT are
trained on large amounts of unlabeled text data and human feedback, and are
capable of generating highly consistent and contextually relevant text across
a variety of domains and tasks, such as robot journalism (Pavlik, 2023),
customer service (Shrivastava, 2023), and healthcare (Sezgin et al., 2022).
Moreover, the generative capabilities of LLMs have been featured as a
potential way to inspire and support creative, in addition to practical,
tasks. For example, prior studies have explored the use of LLMs in supporting
creative writing (Chung et al., 2022; Calderwood et al., 2020), idea
generation (Di Fede et al., 2022), and generating song lyrics (Watanabe et
al., 2018).

Text-to-image models are another type of generative AI model that has been
widely used for a range of applications (Gal et al., 2022). Using deep
learning techniques to generate or modify images, text-to-image models allow
users to create various illustrations, from realistic depictions of landscapes
to abstract art. A well-known example of a text-to-image model is DALL-E 2
(Ramesh et al., 2022), a transformer-based diffusion model that produces
images based on descriptive textual prompts. Other latent diffusion models
such as Stable Diffusion (Rombach et al., 2022) and
Midjourney111https://www.midjourney.com have also been shown to effectively
generate images based on textual prompts. These models have been applied to
various pragmatic and creative application areas, such as fine-art painting
(e.g., r/ImagenAI222https://www.reddit.com/r/ImagenAI,
r/aiArt333https://www.reddit.com/r/aiArt), fashion design (Ak et al., 2019),
and webtoon sketching (Ko et al., 2022).

Generative AI models have been used to make scientific literature more
accessible and efficient to consume. As generative AI models are capable of
producing contextually relevant information (e.g., summaries, visual
representations), researchers have highlighted their use in transforming or
augmenting the reading of scholarly articles to allow a wider audience to gain
knowledge from research. For instance, previous studies have demonstrated the
use of generative AI models for facilitating paper consumption, on tasks such
as paper summarization (Cachola et al., 2020; Li et al., 2020; DeYoung et al.,
2021; Lu et al., 2020), generation of research highlights (Rehman et al.,
2022), and automatic generation of posters or slides (Sefid et al., 2019; Fu
et al., 2021; Xu and Wan, 2022). Not limited to these approaches of supporting
general paper-reading practices, generative AI models have also been shown to
be effective in augmenting domain-specific papers. For example, August et al.
proposed a tool called Paper Plain (August et al., 2022), which leverages an
LLM to lower the barrier to consuming medical knowledge by augmenting medical
papers with features such as section summaries and passage-specific question-
answering.

Motivated by this research, we highlight in this study the potential of
generative AI models in communicating design implications presented in HCI
papers. Sas et al. (Sas et al., 2014) suggested several evaluation criteria
for assessing the quality of design implications: theoretical/empirical
validity (i.e., the level of being academically grounded), generalizability
(i.e., applicability beyond the fieldwork), originality (i.e., ensuring that
the design implication is original), generativity (i.e., ability to open up
new design spaces), inspirability (i.e., ability to stimulate designers to
explore further), and actionability (i.e., ability to be acted upon); that is,
communicating design implications requires the conveyance of both creativity
and academic value. As previous literature has shown the feasibility of
generative AI models in generating both contextually relevant and creative
outputs, we believe that these characteristics will help augment the utility
(i.e., generativity, inspirability, actionability) of the academic paper
without losing its scholarly robustness (i.e., validity, generalizability,
originality) (on Human Factors in Computing Systems, 2023) when generating
design cards, thus assisting designers to more easily consume design insights
from academic papers.

##  3\. Preliminary Study

To understand what designers want to see from design cards for gaining design
insights from academic papers and guiding design, we first conducted an
interview study with 12 designers.

Although there are no fixed rules for designing design cards, several common
components have been used in previous design cards (Aarts et al., 2020),
presumably due to the limited size of design cards, their physical format, and
the emphasis on the visibility of each component. Thus, we began by designing
an initial set of design cards that contain several frequently occurring
components to guide participants in offering feedback.

###  3.1. Initial Design of Design Cards

To choose papers for our initial design cards, we searched in the ACM Digital
Library using keywords related to design implications (e.g., design
implication, design guideline, future design). From the results, we filtered
for full-paper publications from ACM-sponsored conferences/journals that
include design implication sections. Among these, we chose three papers with
non-overlapping topics that offer implications across a range of design
practices (i.e., automotive UX design (Schneider et al., 2021), online
security & safety for children (McNally et al., 2018), and mobile UI design
(Chaudry et al., 2012)).

First, we performed a cursory exploration of the capabilities of generative
models (i.e., summarization, image generation, question answering,
instruction-based text completion, in-context learning) as well as rule-based
techniques (e.g., deriving a bibliographic reference using a paper’s DOI) to
determine if each component of the design card can be generated using these
capabilities. We then prepared a set of initial design cards consisting of
potentially feasible components that are commonly found in existing design
cards (IDEO, 2023; Lockton et al., 2010; Casais et al., 2016; Deng et al.,
2014; Yoon et al., 2016; Colusso et al., 2018). Our initial design cards
included a title, description, image, source text of the design implication,
paper summary (concise overview of the whole paper), and citation. At this
stage, each component was manually generated and prepared by the researchers
except for images, which were created using a text-to-image model (DALL-E 2).
For some components, we offered multiple options to let participants explore
diverging styles (see examples in Figure 2). Descriptions for each component
are as follows:

Figure 2. An illustration of the Miro board participants used during the
preliminary study. Each participant was asked to choose one among several
options for each component, and drag & drop it to a card template to make a
design card.

This figure illustrates the Miro board that participants used during our
preliminary study. This board contains options for title, description, image,
and evidence. The title has four options, which are (i) a verb-based title,
(ii) a gerund-based title that starts with a gerund, (iii) a short noun-style
title, and (i) a long and more descriptive noun-style title. Similarly, the
description has three options: (i) short, (ii) mid-length, and (iii) long
description. The image had four options: (i) abstract and black & white, (ii)
realistic and black & white, (iii) abstract and colored, and (iv) realistic
and colored. Lastly, the evidence had two options: (i) a raw evidence text
with direct quotes and (ii) a two-line summary of the evidence. Participants
can drag and drop each component into the card template to make a design card.

##### Title

The title of the design card is meant to provide an easy-to-understand, high-
level description of the design implication. To understand the designer’s
preference around the title’s actionability, length, and grammatical format,
we set up four types of titles for each paper: verb-format, gerund-format,
long-noun, and short-noun. Verb- and gerund-format were designed to describe
an action of a design guideline (e.g., Make widgets bigger and visualize
interactions / Designing bigger widgets and visualizing interactions), while
the goal of long- and short-noun was mainly to convey the design target and
context in a keyword (e.g., Widgets and interactions / Big widgets and
visualized interactions).

##### Description

The description of the design card aims to offer detailed guidance on
following the design implication. To probe the details that designers want to
see in the description of a design implication, we decided to let designers
explore the formats of this component by preparing three options for the
description: short (approx. 10-15 words), mid-length (approx. 20 words), and
long (approx. 30 words) descriptions, each of which varies in the level of
detail.

##### Image

Image is a visual illustration that is included to inspire and to help
designers gain a sense of the context of a design implication. Prior
literature suggested that the use of color in images has been shown to capture
the attention and interest of viewers, yet it can also be distracting and
hinder comprehension of information (Lamberski and Dwyer, 1983). Similarly,
although photorealistic images offer a higher level of believability,
abstracting an image is frequently employed to effectively communicate image
cues and enhance the viewer’s perception of the image’s subject (Haeberli,
1990). To test these two dimensions in design cards, we prepared images by
varying two stylistic dimensions: abstractness and color.

Specifically, we decided to use a text-to-image model instead of retrieving
from existing image repositories, because academic papers often introduce new
concepts and use cases for which there is no suitable existing illustration.
We set up combinations of two specifications for each dimension, resulting in
four styles: abstract colored, abstract black & white (B&W), realistic
colored, and realistic B&W. Using several prompts involving the paper title
and specified style, we generated images for each option in our study using
DALL-E 2 (Ramesh et al., 2022).

##### Paper summary

As each design implication is rooted in the research context, we believe it is
important to present the study context in the design card. Thus, we summarized
the abstract of the source paper in two sentences and included it in our
design. Unlike other components, we did not present multiple options for the
paper summary.

##### Evidence of the design implication

The purpose of evidence is to let readers review the foundation that underpins
the design guideline. From a part of the paper that contains the result of the
study, we identified a paragraph providing evidence that supports the design
guideline. Particularly, we wanted to determine whether designers would feel
the design card to be more grounded when given the original evidence text from
the paper versus a summary of the paragraph written in plain language. Thus,
we prepared two options that can potentially be used as evidence: raw text of
the evidence with direct quotes and a summary of the evidence written in plain
language.

##### Citation

In cases where designers want to more closely examine the paper, we included a
reference to the paper at the bottom of the design card. This could be
designed as a clickable link to the paper if the card is presented in digital
format. Similar to the paper summary, we also provided only one version of the
citation in APA format.

###  3.2. Recruitment & Participants

To recruit designers, we posted our study recruitment in two design-focused
online communities at our university. We recruited 12 participants (Table 2):
7 of the participants had more than 3 years of prior design work experience, 3
participants had 1–3 years of experience, and 2 participants had no prior work
experience. Of these participants, 7 were working at a company as a designer,
and 5 were pursuing a design degree at the time of the study. The average age
of participants was 25.3 ($SD=3.4$); 9 self-identified as female, 2 non-
binary, and 1 male.

###  3.3. Study Procedure & Analysis

We prepared a Miro board (a collaborative diagramming tool) for each paper
option, which contains (i) the options for each component and (ii) a card
template where participants can drag & drop options to build a design card.

Studies were conducted remotely on Zoom. We first consented participants to
the study and then briefly explained the goal of the study and the design
cards to each participant. Using the Zoom chat functionality, we asked
participants to choose one among the three paper titles used in this study
that is most relevant to their design interests. We sent participants the
paragraph of the paper that contains the design implication and asked them to
read it.

Following, we sent participants a web link that directed them to the Miro
board of the paper they chose. Once they were on the Miro board, for each
design card component, participants were asked to drag & drop one among
several options they prefer the most to the card template, and explain their
reason for choosing it (Figure 2). We then asked them to evaluate the design
card based on metrics for evaluating design implications (Sas et al., 2014)
(i.e., empirical / theoretical validity, generalizability, originality,
generativity, inspirability, and actionability), and discuss any suggestions
for enhancing the design cards based on each metric. Subsequently, we showed
participants the design cards for the other two papers with the same
configurations they chose, and asked them to evaluate the other cards as well,
to help enrich our evaluation.

Each interview lasted approximately 45 minutes. Once complete, each
participant was compensated 20 USD for their participation. All the procedures
of our study were reviewed and approved by the IRB of our university’s human
subjects division.

To analyze the data, we first transcribed the voice recordings. Then, we
analyzed the results by using thematic analysis (Braun and Clarke, 2006),
where the researchers first identified and categorized the significant themes
from the responses. Then, we discussed the initial themes and refined them
until we reached a consensus on the final themes.

###  3.4. Results

We first outline the themes related to the overall design of design cards.
Following that, we report the themes categorized by each design card
component.

####  3.4.1. Overall structure and design of design cards

Overall, participants were satisfied with the types and structure of
information in our design card: “I like the structure (of design cards). I
like how title, description, and all the components make sense with each
other.” (P7) At the same time, we identified potential improvements around the
visual representation of the design card:

##### Distributing textual information to avoid information overload

First, displaying every component on a single side of the design card made it
difficult for participants to read. As our initial design card template puts
all content on a single page, this was reported to affect the visibility of
each component, ultimately making the readers overwhelmed and hesitant to dig
deeper: “(On a single page) there are a lot of texts, and that’s something
that I am overwhelmed with before reading (each component).” (P3)

To resolve this, we explored the potential of using the back side of design
cards to distribute the textual information. Participants mentioned that it
would make the cards less overwhelming to put important components on the
front and the rest on the back side: “Let’s say that we have another side of
this card, and we can include some stuffs in another side of the design card.”
(P8)

##### Importance of visual aesthetics for augmenting inspirability

In addition, participants described the importance of visual aesthetics (e.g.,
vivid background color) for inspiring designers. They reported that the
visually pleasing design of cards is a major factor that induces them to take
a closer look at contents and think through them: “I want the cards to be more
visually attractive and it will make me feel creative and inspired (…) I once
saw very pretty design cards and they made me want to use them.” (P2)

####  3.4.2. Title

##### Title as a means of guiding action without being directive

As the title is the first component that designers read while glancing at the
design cards, participants spoke to the importance of the title in delivering
actionable insights in a concise way. In other words, instead of simply
offering keywords (nouns) related to the design context or subject, they
preferred to read which action they should take from the title. For these
reasons, 10 out of 12 participants preferred either gerund or verb format, as
these grammatical constructs are perceived as actions: “Seems like they (verb
and gerund) are telling me what I’m supposed to do so they are clearer.” (P6)

At the same time, participants mentioned that titles starting with a verb are
too directive, potentially constraining the scope and generalizability of the
use of design cards in their future designs. Instead, gerund format was
mentioned as a way of delivering guidance to act, without forcing the readers
to follow the action: “Verb seems like it’s directing me to must follow a
certain thing like as expecting (…) so I think gerund format would make more
sense.” (P5)

####  3.4.3. Description

Designers mainly viewed the role of description as a ‘tool for understanding
the title more in a detailed way.’ Specifically, we elicited from designers
several necessary elements of a description that they wished to see:

##### Desire to know about the target users

Participants reported that the target user of the design implication (e.g.,
low-literacy population, drivers, children) should be clearly noted in the
description, unless it is general users. This is reported to help them
identify the scope and limitation of the design implication, and determine if
the design implication is applicable to their current designing target: “The
target user should be included because I would want to know who I’m doing it
for.” (P10)

##### Identifying the context (i.e., design subject) in which the design
implication can be applied

Similarly, design subject (i.e., specific object or system that the design
implication can be applied to) was reported as an essential component that
should be clearly indicated in the design implication description. It could be
a physical object (e.g., widget, ride), or an intangible system (e.g.,
monitoring systems): “(I prefer mid-length because) it has a context of which
system designers are designing.” (P7)

##### Justification for following the design implication

Lastly, they would like to see ‘why’ they should follow a certain design
implication. Even though citations make the design implication believable,
having a reasoning of why the design should be a certain way (e.g., Designers
should AAA, in order to BBB) helps them perceive the implication as valid and
gain more confidence in following the recommendation: “(in a short
description) There is no justification for why you should follow it (…) I
won’t believe in following the guideline without justification.” (P2)

####  3.4.4. Image

##### Representing a potential usage context

As an image is the only visual illustration among the components, participants
mentioned the importance of images in imagining the overall context of the
design implication. Thus, they wanted to see how the potential users might be
using the design where the design implication can be applied: “If someone’s
not immediately reading all of the text, just looking at the image should give
them a context or like they’re able to imagine a picture themselves in this
scenario where the design implication is applied.” (P10)

##### Color to inspire creativity, but without distorted representation

Except for one participant, everyone responded that they preferred colored
illustrations for images, by speaking out to the importance of using color in
an image to stimulate inspirability: “It (colored image) makes it visually
appealing (…) the most amount of my attention is very clear when it’s in color
in some way.” (P6) However, participants also expressed concern about the
potential for distorted elements in realistic images after noticing distorted
representations of human faces, which is a known issue in AI-generated images
(Heusel et al., 2017; Borji, 2022). Therefore, they preferred colored images
for the inspirational value, but more abstract as to not compromise image
authenticity: “I noticed those faces (distorted).. maybe an abstract one is
better if you can’t do realistic.” (P3)

####  3.4.5. Paper summary, evidence of the design implication, and citation

Overall, participants found the paper summary and citation presented in the
design card to be necessary. First, a summary of the paper was reported to
help them better understand the overall context of the paper that the design
implication is built on: “I really like the paper summary because I can
actually see what the study is about.” (P4) Also, the citation helped them
perceive that the design implication was from a believable source: “(By seeing
the citation) I feel like it’s grounded and build trust on the contents.” (P6)

For the evidence of design implication, a majority of the participants (8 out
of 12) preferred the raw text with direct quotes instead of a summary in plain
language, due to the credibility of the format: “It (direct quotes for the
evidence) makes the design implication more formal and credible.” (P1)

##  4\. Design Iteration & Implementation

Figure 3. Overview of the pipeline for generating design cards using
generative AI models. In this case, we use GPT-3 as the LLM and DALLE-2 as the
text-to-image model.

This figure illustrates the pipeline for generating design cards using
generative AI models. The title and the description are generated by feeding
the original paper’s title and the design implication text to an LLM using a
zero-shot instruction. The image is generated by first feeding the original
paper’s title and the design implication text to an LLM using a few-shot
instruction to generate an image prompt, which is then fed into a text-to-
image model. The paper summary is generated by feeding the paper abstract into
the LLM and prompting the model to summarize it. The evidence is generated by
first indexing the paper text into an array of paragraphs, and running the Q&A
task with in-context learning using LLM to get the index of a paragraph used
for querying. Lastly, the citation is generated by sending the API request to
doi.org API with the DOI as a parameter.

From the preliminary study, we uncovered insights on how to better structure
and design the cards to support designers’ understanding and potential use of
the design implications. Based on these results, we iterated on the design
card layout and content. We also implemented a pipeline to automatically
generate design cards from academic papers, which we discuss below. An example
design card generated by our system is illustrated in Figure 1.

###  4.1. Overall Structure and Design

First, to alleviate information overload stemming from displaying all
information on a single side of a card, we extended our design to use both
sides of a card. Specifically, we put core design card components (i.e.,
title, description, image) on the front side, and all the other components
(i.e., paper summary, evidence, citation) on the backside.

In addition, to improve upon aesthetics and achieve consistent styling, we
programmed our system to match the background color of the front side with the
most dominant color of the generated image. Specifically, our system obtains a
dominant color from the generated image using the modified mean cut quantizer
technique (Bloomberg, 2008), adjusts the color to a high level of saturation
and low level of value in the HSV color dimension, and sets this as the
background color. This results in a vivid and visually engaging front side
with a clearly readable card title and description.

###  4.2. Generating Card Components

####  4.2.1. Title

Given participants’ input, we focused on making the title actionable. As such,
we decided to generate the title using an LLM with an instructional prompt
specifying that the output include an action verb in the form of a gerund
phrase.

While engineering the prompt, we noticed that some paper authors included the
target context of their design (e.g., user, design subject) in the paper title
while omitting them in their design implication text. Simply relying on the
design guideline text may omit salient keywords from the paper, so we also
included the paper title in the LLM prompt to encourage centering of the
output around the keywords in the title (Appendix B.1). To verify that the
output is formatted as intended, we programmatically detect that the starting
word of the output is in gerund format.

####  4.2.2. Description

For the description, we focused on surfacing the target user, design subject,
and justification for following the design implication. Thus, similar to the
title, we set up a prompt instruction for an LLM to generate a one-line
description for designers such that the output includes these elements
(Appendix B.2.1). To check if the output satisfies the requirements we
included in the prompt instruction, we used the LLM to evaluate its own output
(inspired by work such as (Wu et al., 2023; Bai et al., 2022; Liu et al.,
2023)). Specifically, we programmed our system to verify the results as
another classification task using an LLM. For example, to ensure that the
justification for design is included in the output description, the system
prompts the model to verify as shown in Appendix B.2.2. If one of our three
requirements are not met, we prompt the system to generate three additional
outputs and select from among them the best output based on the same criteria.

####  4.2.3. Image

To generate an image, we used both an LLM and a text-to-image model.
Specifically, (i) we first prompt the LLM to generate an image prompt that
illustrates the context of the design implication; (ii) we then use the text-
to-image model with the prompt output from (i) to generate an image.

For implementation, we engineered a few-shot prompt that contains three
training pairs of source design implication text and the corresponding text-
to-image prompt describing its usage context (Riche et al., 2010; Kim et al.,
2023; Kulp et al., 2019). We noticed, through our prompt engineering
explorations, that jargon terms (e.g., MOOC; massive open online course) were
often difficult for text-to-image models to interpret and were more likely to
cause inaccurate representations in the generated images, so we added
instructions to the prompt to avoid jargon in the output of (i) (Appendix
B.3). We augmented the output of (i) with a styling prompt instructing the
text-to-image model to produce an abstract colored image. Specifically, we
decided to have our system generate an image without including complex,
realistic details by presenting an image in a cartoon style. As the model
generates a colored image by default, we designated the styling of a colored
image by prepending "A cartoon of" to the output prompt from (i). Finally, we
generated an image by feeding the complete prompt as input into the text-to-
image model.

####  4.2.4. Paper summary

To generate the summary of the paper, we instructed an LLM to summarize the
abstract of the paper in two sentences using the following prompt. Here, as
the papers are often written in the first person, we engineered the prompt to
summarize in the third person (Appendix B.4).

####  4.2.5. Evidence

The system converts the XML structure into an array that contains every
paragraph of the paper, matched to its section title. Then, we prompted an LLM
to run a Q&A task by asking for evidence for the design implication text
(Appendix B.5), and find out which source paragraph was used to generate the
output. We used the LlamaIndex444https://github.com/run-llama/llama_index
library to overcome the prompt-length limitation of an LLM and enable in-
context learning. Lastly, we programmed the system to use the selected
paragraph as evidence for the design implication, appending the section number
for reference.

####  4.2.6. Citation

We used the doi.org API to construct a citation for each paper. We designated
APA as the citation format in the API request header. For some papers that do
not have a DOI, our system includes the paper title as well as the author list
as a citation.

###  4.3. Implementation

Our system is implemented as a web interface, where the user can upload a
paper PDF and choose the design implication text from which to generate a
design card. While our generated cards are primarily targeted to designers,
our interface can be used by any user group—authors, designers, science
communicators, and even the general public. The interface is built on a
Javascript framework (SvelteKit), which is connected to a Python backend
server deployed on AWS EC2 that generates and returns the components of the
design card.

More specifically, the server takes as input (i) a PDF of the paper and (ii) a
selected design implication text, and outputs a design card in HTML format.
First, the system uses the Grobid (Lopez, 2009) library to parse the paper PDF
file into the structured XML format, and the system uses the
BeautifulSoup555https://www.crummy.com/software/BeautifulSoup/ library to
process the XML structure and extract components (i.e., title, abstract, DOI,
paragraphs) used to generate the design cards. The overall pipeline for
generating design cards is illustrated in Figure 3.

For the system implementation tested in our evaluation, we used GPT-3 as our
LLM for generating all textual components, and DALL-E 2 for images. These
generative AI models are connected to the backend server through an API. For
all GPT-3 API requests, we used the text-davinci-003 model with the following
parameters: temperature: 0.5, top p: 1, frequency penalty: 0, presence
penalty: 0, best of: 1. We selected these models as they are strong state-of-
the-art models for text and text-to-image generation, though we make no claims
that they are the optimal models for these tasks. Other models could be easily
adapted for use in our modular pipeline.

##  5\. Evaluation

To evaluate the feasibility of communicating design implications using design
cards generated by our system, we conducted a survey study with designers
($N=21$) and authors of HCI papers ($N=12$).

###  5.1. Study Setup & Procedure

####  5.1.1. Survey study with designers

In order to understand the perception of designers toward the design cards
generated by our system, we set up a within-subjects survey study with
designers as follows:

First, we used the same approach as we did in the preliminary study to
identify HCI papers in the ACM DL containing design implications. We chose 4
HCI papers with varying themes and research methods for our survey study with
designers (Table 1; paper 1 – 4). For each paper, we selected a segment of
paper text that contains a design implication; if there are multiple
implications, we randomly chose one among them. Then, we fed the segment into
our system and generated a design card for each paper.

Each participant in our study conducted a survey in our online survey
interface. Each participant was asked to evaluate two papers that were
randomly chosen, and for each paper, was presented two formats for
communicating the design implication: (i) raw text format (a part of the paper
that contains a design implication) and (ii) design card format. Here, the
only markup presented in the raw text condition was citation markers; a link
to the original paper was provided in both conditions to enable participants
to reference the original paper when needed. Each evaluation contains six
questions for evaluating the perceived qualities of the design implication
(Sas et al., 2014) (i.e., empirical/theoretical validity, generalizability,
originality, generativity, inspirability, and actionability) on a 7-point
Likert scale. To ensure participants’ consistent understanding of the
evaluation metrics, our questionnaire incorporated the definitions outlined in
the original paper (Sas et al., 2014) (e.g., generativity – to create and open
up new design spaces; p. 1978). In addition, we randomized the order of both
papers and formats for every participant to avoid the ordering effect.

Once participants finished evaluating formats for the two papers, they were
directed to a screen where they were asked their preferred format for
consuming design implications from academic papers. They were also asked to
provide the reasons for their preference, as well as suggestions for future
enhancements for the design cards.

To recruit designers for our survey study, we posted our recruitment in two
design-focused online communities at our university. In this posting, we
provided an explanation and goals of our study, along with a link directing to
our online survey. As a result, 21 designers completed the survey—13 were
working as designers, 7 were pursuing a degree in design, and 1 was
unemployed. The average age of the participants was 26.5 ($SD=5.7$); 14 of
them were female, 6 male, and 1 preferred not to say. Of all participants, 10
responded that they had heard of the concept of a design card before.

Table 1. Papers used for generating design cards and running the evaluation

| Theme | Venue |  | Research method
---
1 |  | Accessibility,
---
game
CHI ’22 | Qualitative | (Mason et al., 2022)
2 |  | Healthcare,
---
voice agent
TiiS, ’22 | Mixed-methods | (Brewer et al., 2022)
3 |  | Web search,
---
UI design
CHI ’20 | Quantitative | (Baughan et al., 2020)
4 | Chatbot | DIS ’21 | Quantitative | (Svikhnushina et al., 2021)
5 | Healthcare | CHI ’20 | Mixed-methods | (Kim et al., 2020)
6 | Online survey | CHI ’19 | Quantitative | (August and Reinecke, 2019)
7 |  | Accessibility,
---
interaction technique
| MobileHCI
---
’21
Quantitative | (Ahmetovic et al., 2021)
8 |  | AI fairness toolkit
---
FAccT ’22 | Qualitative | (Deng et al., 2022)
9 | Online community | CSCW ’20 | Quantitative | (Xia et al., 2020)
10 | Authoring tool | UIST ’22 | Mixed-methods | (Suh et al., 2022)
11 |  | Social VR,
---
online interaction
CSCW ’21 | Qualitative | (Freeman and Maloney, 2021)
12 |  | Accessibility,
---
voice-based CA
CHI ’21 | Qualitative | (Cha et al., 2021)

This table includes the list of papers used for our study. 12 papers are
listed in this table, and each paper has its corresponding theme, venue,
research method, and citation as columns.

####  5.1.2. Survey study with the authors of HCI papers

To understand if our system can be an effective tool for the paper authors to
communicate design implications, we contacted the authors of HCI papers asking
them several open-ended questions via email. In addition to the four papers
that we used for the survey study with designers, we decided to contact the
authors of additional papers from more diverse domains to gain richer and
potentially more generalizable insights into their perception of the design
cards. In the email, we included a design card that we generated using their
paper, along with the corresponding section of the paper used for generating
it. Based on these contents, we asked each author to (i) assess the accuracy
and clarity of the contents of our design card and (ii) provide any feedback
for future improvement to make AI-generated design cards better support
communication of design implications. We received responses from the authors
of 12 papers in total, including from the authors of all 4 papers that were
used for the survey study with designers, as well as 8 other respondents
(Table 1; paper 1 – 12).

###  5.2. Analyses

####  5.2.1. Quantitative analysis

In our survey study, each participant evaluated the design implications and
their corresponding design cards for two (randomly assigned) papers. Thus, to
analyze our quantitative results while controlling for the random effect and
the paper ID, we used a linear mixed-effects model to test for a significant
difference across the perceived qualities of design implications from reading
the two formats. Specifically, we modeled this by including the paper ID as a
control variable and the participant ID as a random variable.

Figure 4. Perceived qualities of design implication for each format using
metrics from Sas et al. (2014) (Sas et al., 2014). Significance levels are
based on the generalized linear mixed-effects modeling, and the bars indicate
standard errors.

This figure illustrates the perceived qualities of a design implication for
design cards and raw texts from our study. It contains the evaluation of
validity, generalizability, originality, generativity, inspirability, and
actionability. Each measure is measured on a 7-point Likert scale. For the
validity, generalizability, originality, and actionability, there was no
significant difference for both design card and raw text conditions. For
generativity and inspirability, the design card condition showed a
significantly higher level compared to the raw text condition.

####  5.2.2. Qualitative analysis

Once the studies were complete, we then followed the same approach as we did
with the preliminary study data to develop themes. In Subsection 5.4, we refer
to each participant as D1 – D21 (designers) and A1 – A12 (authors), with
participant order randomized.

###  5.3. Quantitative Results

####  5.3.1. Preference on the format

Our survey revealed that the majority of designers preferred to consume a
design implication through design cards. In the survey results, 15 designers
responded that they preferred to consume design implications using a design
card format, while 3 preferred the raw text format; the other 3 designers had
no preference.

####  5.3.2. Perceived qualities of design implication

The linear mixed-effects modeling of our survey results revealed that
designers perceived the content from the design card format as more inspiring
($M=5.21$, $SD=1.22$) and generative ($M=4.60$, $SD=1.17$), compared to the
raw text format ($M=4.33$, $SD=1.75$; $M=4.14$, $SD=1.42$) (Figure 4). The
analysis revealed that there was a significant difference in the perception of
inspirability ($t=3.53$, $p<0.001$) and a marginally significant difference in
the perception of generativity ($t=1.74$, $p<0.1$).

At the same time, the results showed that design cards were able to deliver a
design implication to designers without sacrificing its validity ($M=4.88$,
$SD=1.53$ vs. $M=4.79$, $SD=1.51$; $t=0.35$, $p=0.73$), generalizability
($M=5.02$, $SD=1.30$ vs. $M=4.81$, $SD=1.35$; $t=0.81$, $p=0.42$), or
originality ($M=4.33$, $SD=1.57$ vs. $M=4.31$, $SD=1.47$; $t=0.09$, $p=0.93$).
There was also no significant difference in actionability between design cards
and raw texts ($M=4.55$, $SD=1.76$ vs. $M=4.38$, $SD=1.79$; $t=0.48$,
$p=0.63$).

###  5.4. Qualitative Results

####  5.4.1. Visual hierarchy and visual elements made design cards appealing
and easy to consume

Participants reported the card format to be well-structured, with a clear
visual hierarchy that facilitated information processing. They perceived
information to be well-organized, improving the readability of the contents:
“I liked the cards mainly due to their visual hierarchy as I could know what
is important and what is not.” (D2) Having the key information (i.e., title)
upfront and background contexts on the back side, design cards allowed
participants to quickly gain a sense of a design implication, while allowing
them to consume other information if needed: “I could easily digest by getting
an at-a-glance summary of the insight with the title and then clear divisions
of a longer explanation of the insight with factual references below.” (D18)

In addition, designers from our study evaluated the design of design cards
(e.g., color, image styling) as visually clear and appealing, which was cited
as the reason for their inclination to read the card: “I loved the colors (of
design cards)” (D16); “I enjoyed seeing a visual graphic, which led me to
prefer design cards over plain texts.” (D11) Moreover, the images of design
cards were reported to successfully play a role in assisting the designers to
easily grasp the whole context of a design implication. Designers mentioned
that the context depicted in an image was catchy, enabling them to easily
consume the design implication: “The art on the cards successfully served its
purpose of illustrating what information was being shared (in the paper).”
(D14) Authors also highlighted the image’s potential use of assisting readers
in comprehending the connection between the target user and the design context
of the paper: “The picture describes the relationship and interaction between
[target user of the paper] and [design artifact] well. The most eye-catching
part.” (A9)

####  5.4.2. Our design cards afforded digestibility and ease of understanding
through concise language

The raw text format was perceived as more difficult to read. With a long and
dense text block, raw text format was reported to make it harder for
participants to follow the contents, causing them to lose interest easily and
become bored: “I found it harder to absorb a large bit of text from the raw
text.” (D14); “I felt the raw text format is too boring to read.” (D7) In
contrast, participants found the card format easy to understand and digest
compared to the raw text format. They appreciated the use of concise language
enabled by automated summarization, which helped them easily identify key
points and main takeaways: “Design insights were given to me without me having
to read too much or decipher from the source text.” (D21)

Aligning with the perception of designers, the authors of the paper who
responded to our questions similarly reported the clarity and effectiveness of
design cards to be satisfactory. They also expressed satisfaction with the
idea of automatically generating design cards, noting that these cards
accurately reflect their design implication well: “(The design card) correctly
reflects what we stated in [section] and the wording of the [description] is
relatively similar to the text in the paper - it’s good.” (A8); “The idea is
very cool and I think your system does a good job in summarizing the design
implication from [section] in a crisp and concise way.” (A6) With such well-
aligning contents presented in a convenient format, authors viewed design
cards as an effective means of communicating their design implications: “The
generated design card communicates the design implication listed in [section]
of our paper effectively.” (A2). At the same time, authors did suggest a few
potential refinements of generated texts in our design cards, such as reducing
factual misalignment from the paper summary when combining multiple research
findings simultaneously to achieve brevity (A6) and enhancing descriptions to
more accurately reflect the role of the design target (A5).

Based on the clarity and effectiveness of the format, the authors highlighted
the potential of design cards for strengthening their contributions around the
design implications. By collaborating with our design card system, authors
believed that our system could be a great authoring tool for augmenting the
delivery of their paper contributions: “If researchers were to go through one
or two revisions based on this approach, it could indeed greatly enhance the
delivery of their contribution much strongly and in a more meaningful way.”
(A3)

####  5.4.3. Our system supported communication of design implications without
loss of validity and originality

From our quantitative analysis, we identified that design cards delivered a
design implication without sacrificing any validity and originality. Aligning
with these results, design card and raw text formats were both reported to be
valid and believable, even if the design card was programmatically generated
from the original paper text. In particular, we identified that the inclusion
of extracted evidence and the paper citation contributed to making the design
card a reliable source for design insights. Such cues implying that the
results were verified led participants to perceive the design card as more
legitimate: “Seeing the parts on the bottom (evidence, citation) made it look
professional.” (D19)

At the same time, the survey with the authors revealed the need to refine the
pipeline for extracting evidence for a design implication. To identify
evidence from the paper, we prompted an LLM to run a Q&A task and programmed
our system to use the paragraph of a paper that was used to derive the answer,
which was reported to be an effective way to deliver the author’s reasoning of
the design guideline: “I like (evidence)… It helps effectively communicate the
rationale for the design implications.” (A11) Yet, four authors mentioned that
the evidence included in the design card of their paper could be potentially
enhanced, by pointing out that it was partially correct, not the only evidence
present in the paper, or misleading: “The part about where the guideline came
from seems off.” (A5) As a potential mitigation strategy, one author offered
that having a rule-based benchmarking tool to verify how much the evidence
overlaps with the design implication might help: “Some checks based on word
overlap between the generated guideline and the text in the referenced paper
paragraph could be helpful to get a proxy for the accuracy.” (A2)

####  5.4.4. Opportunities for enhancing the use of images

Our analysis revealed that both designers and authors perceived the use of
auto-generated images to help support the communication of design
implications: “The approach of using an AI-generated image for authoring the
design implication is interesting and will be super helpful (when
communicating design implications).” (A3) At the same time, our interview also
revealed some potential uses for additional images in a design card to help
provide more information about the paper or exemplify the design implication.
For example, designers mentioned that adding infographics depicting results
from the paper would further help them to understand the study: “I also want
to see a pure infographic for the results.” (D13) One author also suggested
using a figure directly from their original papers to help illustrate the
design guideline as was envisioned: “I think showing a representative figure
of the paper can be helpful, especially if that figure/artifact exemplifies
the design mentioned in the design implications or can further clarify.” (A11)
Another author mentioned that there are multiple key messages they intended to
communicate in their design implications section, but that the single image
shown reflected only one of them: “I feel that the image only covers a subset
of the design implication presented in the text and may not necessarily
highlight all the key messages.” (A4) This suggests iterating on our card
designs to support the inclusion of multiple images per design card.

(a) Imperfect match between content and evidence

(b) Improper representation of the target users

(c) Unintended emotion of the target user

Figure 5. Examples of misalignments surfaced in our survey

These figures illustrate three of the misalignments that were identified from
our survey. The first subfigure illustrates an imperfect matching of the
evidence of our design cards, the second subfigure illustrates a
misrepresentation of the target users in an image, and the third subfigure
illustrates an unintended emotion of the target user in an image.

Despite the generally positive perceptions towards the generated images, a few
authors did note some misalignment between their expectations toward the image
representation and the actual image (e.g., Figure 5-b, c). For example, there
was a misrepresentation of the target users in an image that depicts people
with disabilities, which the author found to be inappropriate (A12). Other
authors also noted a misalignment between their intent and the output. For
example, A1 pointed out the misalignment between their expected emotion toward
potential users and that of the user illustrated in the design card image.
Similarly, after seeing a card image featuring two users where only one was
using a computing device, A10 clarified that the intended representation
should depict both users engaging with the device to ensure clear
communication of their design implication’s key message of device-mediated
communication and avoid any potential confusion.

##  6\. Discussion

In this study, we examined the feasibility of AI-generated design cards as a
tool for communicating design insights between paper authors and designers.
Our system automatically produces design cards based on a design implication
from a paper, and demonstrated its potential to assist authors in
communicating their design implications. We expect that a more effective
process for presenting design implications could help alleviate the burden on
paper authors to communicate their findings to design practitioners. Bridging
this translational gap can further enhance the impact of valuable design
insights from academic papers.

Results from our evaluation suggest that designers perceived our AI-generated
design cards as more inspiring and generative compared to traditional paper
formats. We also found that these gains were had without sacrificing the
validity, originality, and generalizability of the design finding. A key
reason for this that we identified in our qualitative analysis is that our
system lets designers view the original text via evidence quotes and
citations, which ultimately assured them that design card contents were
legitimate. In addition, we believe that our design choice of abstracting and
stimulating the visual element (i.e., abstract, colored representation of the
image)—factors known to inspire designers as suggested by Sas et al. (Sas et
al., 2014)—has also contributed to such results. However, our survey with
paper authors revealed that extracted evidence for a design implication can
sometimes be inaccurate or not perfectly aligned with the part of the paper
authors used to derive the design implication. As such a misalignment might
affect the overall credibility of generated design cards, future systems
should focus on validating the accuracy of represented information. Involving
authors in the process of generating design cards could help guarantee the
validity and appropriateness of outputs.

Our results also suggest that there are opportunities to involve the paper
authors in enhancing the use of images on the design cards. Despite the
efficacy of AI-generated images in providing an overall context of the design
implication, a few authors from our study reported a misalignment between the
card images and their expectations based on their original intent (e.g.,
unintended emotion of the depicted user), or expressed a wish to include a
representative figure directly from the paper to further help readers’
understanding. To address this, we believe that including authors in the loop
may help to better reflect their original intent; for instance, the system may
offer multiple image prompt candidates, and let authors choose the one that
best aligns with their intended message. Likewise, the system may complement
our AI-generated image by detecting several representative figures from the
paper and letting authors choose and add one that best exemplifies their
intended message. As such, we believe that our system could further ensure the
alignment between images and the author’s intent, without requiring
significant efforts.

Despite the effectiveness of our system, one remaining question is how to make
the content of design cards generated by our system more actionable. Although
we designed our title to involve action, our survey with designers revealed no
significant difference in actionability between the design card format and raw
text format. One possible reason is that the papers’ design implication
sections (our study comparisons) were already well-written and were calling
for designers to take a particular design action. Thus, designers did not
perceive a difference in actionability. However, our study does suggest some
changes that we can explore to make the design card even more actionable, such
as having an additional component that gives more detailed guidance (e.g.,
step-by-step guidance for designing) or examples of connecting the design
implication to real-world artifacts (e.g., how a well-known design was/can be
enhanced by applying this implication).

Regarding the target audience for our system and the generated artifact, we
intended the generated design cards to be specifically read and consumed by
designers. Meanwhile, the use of our interface for generating cards is more
versatile (Section 4.3); we anticipate that various individuals, such as paper
authors, designers, science communicators, or the general public, could find
value in creating cards using our interface. For example, we envision that
authors of newly published papers might utilize our interface to generate
design cards to showcase their work to designers. Also, designers may directly
use our system to create cards in advance of their actual design activities,
enabling the use of these cards for purposes where reading the papers may not
be feasible (e.g., conducting a design sprint or workshop under time
constraints). Investigating how different stakeholders may find value in using
our interface to generate cards is another avenue of research.

In addition to understanding such varying purposes of using our interface,
another potential consideration that will need to be studied is how to make
our card-based translational resource available to designers and improve their
awareness. While our system has the potential to improve designers’ use of the
abundance of rich insights in academic papers, recent research suggests that
designers often lack awareness of the wealth of design cards that exist and
their potential benefits, despite a set of existing design cards that can be
readily used (Roy and Warren, 2019; Hsieh et al., 2023). This awareness
problem could worsen if our system helps generate a large influx of cards. One
possible solution is to integrate our system into digital libraries (e.g., ACM
Digital Library) and present the generated design cards alongside each paper.
Other more designer-friendly repositories should also be explored to help the
dissemination of this type of resource.

##  7\. Limitation & Future Work

There were several assumptions that we made in building and evaluating our
system. First, although we evaluated the qualities of design cards generated
by our system, we did not test how generated design cards would be used in the
real-world designing contexts of designers. From the findings of our
controlled study design which may serve as a crucial first step for examining
such real-world uses, we should further explore how AI-generated design cards
affect the design process and outputs of designers. Future work may also
investigate further customizability and generalizability of our concept by (i)
exploring how to tailor the use of design cards for different purposes and
designer subgroups and (ii) evaluating more papers beyond what we used in our
controlled study.

Our interface also assumes a context where the paper segments containing
design implications need to be manually localized. While design implication
sections are frequently included in HCI publications, future work could
investigate ways to automatically identify text corresponding to design
implications, as well as segment these into multiple distinct implications.
These steps are needed to further streamline the generation of design cards
from HCI papers.

Additionally, although our focus was to demonstrate the potential of AI-
generated cards by choosing reasonable methods, there are many alternate
models, techniques, and prompts that could be used to build our system. Since
our system is modular, each component can easily accommodate alternate
techniques or models to test performance, and we believe this can be expanded
on in future work. Similarly, although our characterization of design card
components was based on our cursory examination of existing design card sets,
there may also exist alternative design components to achieve our goal of
creating useful translational resources for designers (e.g., including
different card elements for inspirational purposes). Investigating other
component options could make these resources richer and allow us to understand
the perception of different types of components per user group.

Lastly, future work should strive to mitigate AI biases. As utilizing AI-
generated images may retain biases from the datasets they are trained on, it
is an open concern (Mishkin et al., 2022; Brown et al., 2020) that the outputs
manifest gender and racial stereotypes (e.g., higher likelihood of featuring
white men rather than representative users; negative associations for certain
racial/ethnic groups). To overcome this, it is necessary to develop methods to
identify and mitigate such biases in future systems.

##  8\. Conclusion

In this paper, we propose an approach of using generative AI models to help
create design cards that distill academic findings into a more accessible and
prescriptive format. Based on the preliminary interview sessions with design
practitioners, we developed a system that automatically generates a design
card using generative AI models. Results from our evaluation with designers
and HCI paper authors demonstrated the potential of AI-generated design cards
in communicating design implications, as well as their future enhancements.

###### Acknowledgements.

This work was supported by an Accelerating Foundation Models Research Award
from Microsoft.

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##  Appendix A Study Detail of the Preliminary Study

Table 2. Participants’ demographic and background information of our
preliminary study

ID | Age | Gender | Design focus / background |  | Design
---
work
experience
(years)
P1 | 30 | Female |  | Masters student in
---
professional design program,
user interface/experience
designer
3 - 4
P2 | 21 |  | Non-
---
binary
| Urban designer, previous
---
experience in urban design
and CS
1 - 2
P3 | 24 | Female |  | User experience designer
---
3 - 4
P4 | 22 | Male |  | Design researcher, user
---
interface/experience designer
3 - 4
P5 | 24 |  | Non-
---
binary
| Masters student in design,
---
previously worked as a visual
designer, BFA in design
¿ 5
P6 | 25 | Female |  | Masters student in
---
professional design program
-
P7 | 24 | Female |  | Masters student in
---
professional design program
3 - 4
P8 | 25 | Female |  | Design researcher
---
2 - 3
P9 | 28 | Female |  | User experience designer
---
3 - 4
P10 | 24 | Female |  | Masters student in
---
professional design program,
previous experience in
software engineering
-
P11 | 23 | Female |  | User experience designer &
---
design researcher, focusing
on accessible designing
1 - 2
P12 | 33 | Female |  | Product designer
---
4 - 5

This table lists the demographic and background information of the designers
who participated in our preliminary study. There are 12 rows in this table,
each of which details the participant’s information, and each row has ID, age,
gender, design focus/background, and design work experience about each
participant as columns.

##  Appendix B Prompts Used for Generating Card Components

###  B.1. Title

The following text snippet is part of an academic paper (title: {PAPER
TITLE}), which contains a guideline for a certain design practice. From this
text, extract a concise title that delivers what action designers should take
in gerund-format (i.e., verb+ing): {DESIGN IMPLICATION TEXT}

###  B.2. Description

####  B.2.1. Generating description text

The following text snippet is part of an academic paper (title: {PAPER
TITLE}), which contains a guideline for a certain design practice. From this
text, extract a one-line argument for designers that contains (i) who the
design is targeting, (ii) what the design subject is, and (iii) the
justification for the design: {DESIGN IMPLICATION TEXT}

####  B.2.2. Verifying description text

Does the following statement contain the {ENTITY (e.g. justification)} for
designers to follow a certain design direction? Answer in YES or NO:
{GENERATED DESCRIPTION TEXT}

###  B.3. Image

Illustrate a one-line usage context for the following design guideline. The
output should not contain any jargon:

Design guideline: Since older adults are unfamiliar with video medium, (...)
Usage context: an elderly person looking at a laptop with a video playing on
it
..
{FEW-SHOT EXAMPLES}
..
Design guideline: {DESIGN IMPLICATION TEXT}

###  B.4. Paper Summary

Summarize the following text in two sentences, describing the authors in the
third person: {ABSTRACT}

###  B.5. Evidence

What can be the evidence for this design implication: {DESIGN IMPLICATION
TEXT}
(with in-context learning of the indexed paper paragraphs)

##  Appendix C Examples of Design Cards Generated from Our System

(a) Kim et al., 2023 (Kim et al., 2023)

(b) Xia et al., 2020 (Xia et al., 2020)

(c) Chaudry et al., 2012 (Chaudry et al., 2012)

(d) Freeman & Maloney, 2021 (Freeman and Maloney, 2021)

Figure 6. Examples of design cards generated from our system

These figures illustrate four sample design cards generated from our system.
The first design card is generated from a paper about older adults’ use of
online videos for learning, and the second design card is generated from a
paper that explores toxicity in online discussion. The third design card is
generated from a paper that speaks to the importance of designing larger
widgets for aiding the low-literacy population in using mobile devices, and
the fourth design card is built on a paper that explores the presentation and
the perception of self in social VR.