Jonas Oppenlaender

Ville Paananen, Jonas Oppenlaender, Aku Visuri

The recent progress of text-to-image generation has been recognized in architectural design. Our study is the first to investigate the potential of text-to-image generators in supporting creativity during the early stages of the architectural design process. We conducted a laboratory study with 17 architecture students, who developed a concept for a culture center using three popular text-to-image generators: Midjourney, Stable Diffusion, and DALL-E. Through standardized questionnaires and group interviews, we found that image generation could be a meaningful part of the design process when design constraints are carefully considered. Generative tools support serendipitous discovery of ideas and an imaginative mindset, enriching the design process. We identified several challenges of image generators and provided considerations for software development and educators to support creativity and emphasize designers' imaginative mindset. By understanding the limitations and potential of text-to-image generators, architects and designers can leverage this technology in their design process and education, facilitating innovation and effective communication of concepts.

Jonas Oppenlaender

Text-to-image generation has seen an explosion of interest since 2021. Today, beautiful and intriguing digital images and artworks can be synthesized from textual inputs ("prompts") with deep generative models. Online communities around text-to-image generation and AI generated art have quickly emerged. This paper identifies six types of prompt modifiers used by practitioners in the online community based on a 3-month ethnographic study. The novel taxonomy of prompt modifiers provides researchers a conceptual starting point for investigating the practice of text-to-image generation, but may also help practitioners of AI generated art improve their images. We further outline how prompt modifiers are applied in the practice of "prompt engineering." We discuss research opportunities of this novel creative practice in the field of Human-Computer Interaction (HCI). The paper concludes with a discussion of broader implications of prompt engineering from the perspective of Human-AI Interaction (HAI) in future applications beyond the use case of text-to-image generation and AI generated art.

Jonas Oppenlaender, Johanna Silvennoinen, Ville Paananen et al.

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.

Jonas Oppenlaender

Humankind is entering a novel creative era in which anybody can synthesize digital information using generative artificial intelligence (AI). Text-to-image generation, in particular, has become vastly popular and millions of practitioners produce AI-generated images and AI art online. This chapter first gives an overview of the key developments that enabled a healthy co-creative online ecosystem around text-to-image generation to rapidly emerge, followed by a high-level description of key elements in this ecosystem. A particular focus is placed on prompt engineering, a creative practice that has been embraced by the AI art community. It is then argued that the emerging co-creative ecosystem constitutes an intelligent system on its own - a system that both supports human creativity, but also potentially entraps future generations and limits future development efforts in AI. The chapter discusses the potential risks and dangers of cultivating this co-creative ecosystem, such as the bias inherent in today's training data, potential quality degradation in future image generation systems due to synthetic data becoming common place, and the potential long-term effects of text-to-image generation on people's imagination, ambitions, and development.

Jonas Oppenlaender, Joonas Hämäläinen

Large language models (LLMs), such as ChatGPT and GPT-4, are gaining wide-spread real world use. Yet, these LLMs are closed source, and little is known about their performance in real-world use cases. In this paper, we apply and evaluate the combination of ChatGPT and GPT-4 for the real-world task of mining insights from a text corpus in order to identify research challenges in the field of HCI. We extract 4,392 research challenges in over 100 topics from the 2023~CHI conference proceedings and visualize the research challenges for interactive exploration. We critically evaluate the LLMs on this practical task and conclude that the combination of ChatGPT and GPT-4 makes an excellent cost-efficient means for analyzing a text corpus at scale. Cost-efficiency is key for flexibly prototyping research ideas and analyzing text corpora from different perspectives, with implications for applying LLMs for mining insights in academia and practice.

Jonas Oppenlaender, Hannah Johnston, Johanna Silvennoinen et al.

Image generation using generative artificial intelligence has become a popular activity. However, text-to-image generation - where images are produced from typed prompts - can be less engaging in public settings since the act of typing tends to limit interactive audience participation, thereby reducing its suitability for designing dynamic public installations. In this article, we explore body prompting as input modality for image generation in the context of installations at public event settings. Body prompting extends interaction with generative AI beyond textual inputs to reconnect the creative act of image generation with the physical act of creating artworks. We implement this concept in an interactive art installation, Artworks Reimagined, designed to transform existing artworks via body prompting. We deployed the installation at an event with hundreds of visitors in a public and private setting. Our semi-structured interviews with a sample of visitors (N = 79) show that body prompting was well-received and provides an engaging and fun experience to the installation's visitors. We present insights into participants' experience of body prompting and AI co-creation and identify three distinct strategies of embodied interaction focused on re-creating, reimagining, or casual interaction. We provide valuable recommendations for practitioners seeking to design interactive generative AI experiences in museums, galleries, and public event spaces.

An Vu, Jonas Oppenlaender

The rise of large language models (LLMs) has created a new job role: the Prompt Engineer. Despite growing interest in this position, we still do not fully understand what skills this new job role requires or how common these jobs are. We analyzed 20,662 job postings on LinkedIn, including 72 prompt engineer positions, to learn more about this emerging role. We found that prompt engineering is still rare (less than 0.5% of sampled job postings) but has a unique skill profile. Prompt engineers need AI knowledge (22.8%), prompt design skills (18.7%), good communication (21.9%), and creative problem-solving (15.8%) skills. These requirements significantly differ from those of established roles, such as data scientists and machine learning engineers, showing that prompt engineering is becoming its own profession. Our findings help job seekers, employers, and educational institutions in better understanding the emerging field of prompt engineering.

Hannu Simonen, Atte Kiviniemi, Hannah Johnston et al.

In the creative practice of text-to-image (TTI) generation, images are synthesized from textual prompts. By design, TTI models always yield an output, even if the prompt contains unknown terms. In this case, the model may generate default images: images that closely resemble each other across many unrelated prompts. Studying default images is valuable for designing better solutions for prompt engineering and TTI generation. We present the first investigation into default images on Midjourney. We describe an initial study in which we manually created input prompts triggering default images, and several ablation studies. Building on these, we conduct a computational analysis of about 750,000 images, revealing consistent default images across unrelated prompts. We also conduct an online user study investigating how default images may affect user satisfaction. Our work lays the foundation for understanding default images in TTI generation, highlighting their practical relevance as well as challenges and future research directions.

Jonas Oppenlaender

Designing a novel product is a difficult task not well suited for non-expert crowd workers. In this work-in-progress paper, we first motivate why the design of persuasive products is an interesting context for studying creativity and the creative leap. We then present a pilot study on the crowdsourced design of persuasive products. The pilot study motivated our subsequent feasibility study on the use of morphological matrices as a tool for crowdsourced ideation and product design. Given the morphological matrix, workers were able to come up with valid and significantly more relevant ideas for novel persuasive products.

Jonas Oppenlaender, Elina Kuosmanen, Andrés Lucero et al.

Feedback is an important aspect of design education, and crowdsourcing has emerged as a convenient way to obtain feedback at scale. In this paper, we investigate how crowdsourced design feedback compares to peer design feedback within a design-oriented HCI class and across two metrics: perceived quality and perceived fairness. We also examine the perceived monetary value of crowdsourced feedback, which provides an interesting contrast to the typical requester-centric view of the value of labor on crowdsourcing platforms. Our results reveal that the students (N=106) perceived the crowdsourced design feedback as inferior to peer design feedback in multiple ways. However, they also identified various positive aspects of the online crowds that peers cannot provide. We discuss the meaning of the findings and provide suggestions for teachers in HCI and other researchers interested in crowd feedback systems on using crowds as a potential complement to peers.

Jonas Oppenlaender, Simo Hosio

Crowd feedback systems have the potential to support creative workers with feedback from the crowd. In this position paper for the Workshop on Designing Crowd-powered Creativity Support Systems (DC2S2) at CHI '19, we present three creativity support tools in which we explore how creative workers can be assisted with crowdsourced formative and summative feedback. For each of the three crowd feedback systems, we provide one idea for future research.

Jonas Oppenlaender, Aku Visuri, Kristy Milland et al.

Crowdsourcing platforms are a powerful and convenient means for recruiting participants in online studies and collecting data from the crowd. As information work is being more and more automated by Machine Learning algorithms, creativity $-$ that is, a human's ability for divergent and convergent thinking $-$ will play an increasingly important role on online crowdsourcing platforms. However, we lack insights into what crowd workers think about creative work. In studies in Human-Computer Interaction (HCI), the ability and willingness of the crowd to participate in creative work seems to be largely unquestioned. Insights into the workers' perspective are rare, but important, as they may inform the design of studies with higher validity. Given that creativity will play an increasingly important role in crowdsourcing, it is imperative to develop an understanding of how workers perceive creative work. In this paper, we summarize our recent worker-centered study of creative work on two general-purpose crowdsourcing platforms (Amazon Mechanical Turk and Prolific). Our study illuminates what creative work is like for crowd workers on these two crowdsourcing platforms. The work identifies several archetypal types of workers with different attitudes towards creative work, and discusses common pitfalls with creative work on crowdsourcing platforms.

Jonas Oppenlaender, Kristy Milland, Aku Visuri et al.

General-purpose crowdsourcing platforms are increasingly being harnessed for creative work. The platforms' potential for creative work is clearly identified, but the workers' perspectives on such work have not been extensively documented. In this paper, we uncover what the workers have to say about creative work on paid crowdsourcing platforms. Through a quantitative and qualitative analysis of a questionnaire launched on two different crowdsourcing platforms, our results revealed clear differences between the workers on the platforms in both preferences and prior experience with creative work. We identify common pitfalls with creative work on crowdsourcing platforms, provide recommendations for requesters of creative work, and discuss the meaning of our findings within the broader scope of creativity-oriented research. To the best of our knowledge, we contribute the first extensive worker-oriented study of creative work on paid crowdsourcing platforms.