Camellia Redko

Emi: User Interface Components for Generating Emotional Images for Mood Boards

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Duration: 16 weeks

My Roles: UX researcher, UX designer

Icon representing methods
Design Thinking User Research User Testing Usability Testing Interviews Surveys Ideation Workshop Affinity Diagramming Prototyping
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Figma Adobe Photoshop

In the spring of 2023, I took the course HCI Research Project in my master's program. Together with Eva-Maria Strumegger and Luzie Schroeter, we developed a prototype that guides users through generating effective prompts for AI-generated emotional images by examining HCI-related scientific literature, formulating a meaningful research question, conducting research through a workshop, developing a prototype based on our findings, and then testing and analyzing this prototype. Our work culminated in a scientific research paper we presented at an HCI conference. This paper can be viewed here.


Users struggle to convey emotions through text-to-image prompts

Text-to-image generators have transformed the creative landscape by converting text descriptions into unique visual representations. However, text as an input modality presents challenges for users due to the need for precise instructions [1]. Conveying emotions through text-to-image prompts is particularly challenging [2], and research that has addressed user needs and the usability of such systems has been limited so far [3, 4, 5].


Provide useful UI components to guide users through generating effective prompts for emotional images

Examples of implemented UI components Ratings of implemented UI components


A way to generate two times more usable results

As my group researched ways to facilitate the creation of effective prompts, we came across the idea of structuring searches to produce prompts. Liu et al. [6], for instance, developed a structured search system called "Opal" to guide users through the generation of text-to-image prompts for news illustrations. The results of their study were striking:

“Users with [the structured search system] generated two times more usable results than users without.”

In contrast to Liu et al., we investigated the usefulness of different UI components that may be used for producing or editing text-generated images, and we were interested in emotionally rich images for mood boards rather than news illustrations.


Produce novel research

After conducting research on related work, we proposed the following research question to guide us:

“What UI components might be useful for a structured search to generate emotional imagery for creative mood boarding using text-to-image generators?”

A moodboard produced with the help of the text-to-image generator Midjourney

We had decided to focus on mood boards because they are a visual medium especially focused on communicating emotions [7, 8, 9], and there was a lack of research in this area.


Gather ideas on such UI components

In order to address our research question, we conducted a co-design workshop to gather ideas on useful UI components. Our participants were Human-Computer Interaction students with previous experience in creating mood boards and using AI image generator tools. Inspired by Design Thinking methodology [10], our workshop was divided into the following phases:

Co-design workshop steps

The following themes emerged from our thematic analysis of participants' input:

1. Context and depicted elements are the basis of emotional images
2. Ambience contributes to images being perceived as emotional
3. Colors convey emotions
4. Objects or subjects that express feelings make an image emotional
5. An emotional image tells a story
6. Personal experiences influence how we perceive emotional images



Our scenario-based clickable prototype, called "Emi," was based on the insights gained from the co-design workshop as well as the design guidelines for prompt engineering for generative text-to-image models developed by Liu and Chilton. It was divided into the following two steps:

Emi prototype


Users rated Emi and its UI components positively

We conducted a three-stage user test to investigate the usability as well as the hedonic and pragmatic qualities of the user experience when using the Emi prototype. Moreover, we examined the perceived usefulness of each UI component when it comes to creating emotional images.

User study

Our evaluation shows that Emi is user-friendly and effective in guiding the creation of emotional images for mood boards, and its hedonic and pragmatic qualities are perceived to be high. Emi received a System Usability Scale (SUS) score of 75.94, an AttrakDiff score of 1.14 for pragmatic quality, and an AttrakDiff average score of 1.19 for hedonic quality.

Ratings of implemented UI components AttrakDiff results


Lessons learned

I am immensely grateful for having had the opportunity to work on such an interesting, exciting research project with Eva-Maria and Luzie that not only deepened my understanding of human-computer interaction but also shaped my approach to design and problem-solving. Reflecting on this, I learned a few valuable lessons:

1. Thorough research early on is essential. As generative AI had become a hot topic for research, our group had to be well-informed of potential directions of development within this field to avoid accidentally replicating the aims of prior research or new research that may emerge during the course of our project. Some of our initial research questions had already been addressed by prior research, and we fine-tuned our research questions until they were meaningfully novel by comparing existing work, drawing inspiration from “future work” sections in other papers, and narrowing down our focus.

2. Don't overlook pilot tests. For our workshops and user tests, we had to present information to users that sometimes may have seemed clear to us but not to them. We found it helpful to continuously receive feedback from each other and others to ensure that users would understand us before presenting information to them.

3. Organization pays off. Our project was documented with the collaborative software tool Miro, and clearly sectioning different parts of the project throughout a timeline for such a huge project greatly facilitated our workflow.


  1. [1] Vivian Liu and Lydia B Chilton. 2022. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 384, 23 pages.
  2. [2] Yunlong Wang, Shuyuan Shen, and Brian Y. Lim. 2023. RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Albrecht Schmidt, Kaisa Väänänen, Tesh Goyal, Per Ola Kristensson, Anicia Peters, Stefanie Mueller, Julie R. Williamson, and Max L. Wilson (Eds.). ACM, New York, NY, USA, 1–29.
  3. [3] Yutong Xie, Zhaoying Pan, Jinge Ma, Luo Jie, and Qiaozhu Mei. 2023. A Prompt Log Analysis of Text-to-Image Generation Systems. In Proceedings of the ACM Web Conference 2023, Ying Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo, and Geert-Jan Houben (Eds.). ACM, New York, NY, USA, 3892–3902.
  4. [4] Michael Muller, Lydia B. Chilton, Anna Kantosalo, Charles Patrick Martin, and Greg Walsh. 2022. GenAICHI: Generative AI and HCI. In CHI Conference on Human Factors in Computing Systems Extended Abstracts, Simone Barbosa, Cliff Lampe, Caroline Appert, and David A. Shamma (Eds.). ACM, New York, NY, USA, 1–7. 1145/3491101.3503719
  5. [5] Han Qiao, Vivian Liu, and Lydia Chilton. 2022. Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated Art. In Creativity and Cognition. ACM, New York, NY, USA, 15–28.
  6. [6] Vivian Liu, Han Qiao, and Lydia Chilton. 2022. Opal: Multimodal Image Generation for News Illustration. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22). Association for Computing Machinery, New York, NY, USA, Article 73, 17 pages.
  7. [7] T. D. Cassidy. 2008. Mood boards: Current practice in learning and teaching strategies and students’ understanding of the process. International Journal of Fashion Design, Technology and Education 1, 1 (2008), 43–54.
  8. [8] Huang-Ming Chang, Marta Boladeras, Andreu Català, Wei Chen, and Matthias Rauterberg. 2014. Mood Boards as a Universal Tool for Investigating Emotional Experience.
  9. [9] Steve Garner and Deana McDonagh-Philp. 2001. Problem Interpretation and Resolution via Visual Stimuli: The Use of ‘Mood Boards’ in Design Education. Journal of Art & Design Education 20, 1 (2001), 57–64.
  10. [10] Annu Sible Prabhakar. 2021. Design Thinking For Technology Design. In Proceedings of the 22nd Annual Conference on Information Technology Education (SnowBird, UT, USA) (SIGITE ’21). Association for Computing Machinery, New York, NY, USA, 153–154. https: //