arXiv subjects

Jonas Oppenlaender

Publications and source records attributed to Jonas Oppenlaender.

At least 19 recordsLinked to original sources

arXiv2026

How Ten Publishers Retract Research

Retractions are the primary mechanism for correcting the scholarly record, yet publishers differ markedly in how they use them. We present a bibliometric analysis of 46,087 retractions across 10 major publishers using data from the Retraction Watch database (1997-2026), examining retraction rates, reasons, temporal trends, and geographic distributions, among other dimensions. Normalized retraction rates vary by two orders of magnitude, from Elsevier's 3.97 per 10,000 publications to Hindawi's 320.02. China-affiliated authors account for the largest share of retractions at every publisher. Retraction lags and reason profiles also vary widely across publishers. Among the ten publishers, ACM is an outlier in its retraction profile. ACM's normalized rate is mid-range (5.65), yet 98.3% of its 354 retractions are related to one incident. Seven of the ten most common global retraction reasons (including misconduct, plagiarism, and data concerns) are entirely absent from ACM's record. ACM's first retraction dates to 2020, despite a catalog dating to 1997. ACM self-describes its retraction threshold as "extremely high." We discuss this threshold in relation to the COPE retraction guidelines and the implications of ACM's non-public dark archive of removed works.

cs.DL↗

arXiv2026

StatCounter: A Longitudinal Study of a Portable Scholarly Metric Display

This study explores a handheld, battery-operated e-ink device displaying Google Scholar citation statistics. The StatCounter places academic metrics into the flow of daily life rather than a desktop context. The work draws on a first-person, longitudinal auto-ethnographic inquiry examining how constant access to scholarly metrics influences motivation, attention, reflection, and emotional responses across work and non-work settings. The ambient proximity and pervasive availability of scholarly metrics invites frequent micro-checks, short reflective pauses, but also introduces moments of second-guessing when numbers drop or stagnate. Carrying the device prompts new narratives about academic identity, including a sense of companionship during travel and periods away from the office. Over time, the presence of the device turns metrics from an occasional reference into an ambient background of scholarly life. The study contributes insight into how situated, embodied access to academic metrics reshapes their meaning, and frames opportunities for designing tools that engage with scholarly evaluation in reflective ways.

cs.HC↗

arXiv2026

Conversational Inoculation to Enhance Resistance to Misinformation

Proliferation of misinformation is a globally acknowledged problem. Cognitive Inoculation helps build resistance to different forms of persuasion, such as misinformation. We investigate Conversational Inoculation, a method to help people build resistance to misinformation through dynamic conversations with a chatbot. We built a Web-based system to implement the method, and conducted a within-subject user experiment to compare it with two traditional inoculation methods. Our results validate Conversational Inoculation as a viable novel method, and show how it was able to enhance participants' resistance to misinformation. A qualitative analysis of the conversations between participants and the chatbot reveal independence and trust as factors that boosted the efficiency of Conversational Inoculation, and friction of interaction as a factor hindering it. We discuss the opportunities and challenges of using Conversational Inoculation to combat misinformation. Our work contributes a timely investigation and a promising research direction in scalable ways to combat misinformation.

cs.HC↗

arXiv2025

Prompt Engineer: Analyzing Hard and Soft Skill Requirements in the AI Job Market

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. In this paper, we present a data-driven analysis of global prompt engineering job trends on LinkedIn. We take a snapshot of the evolving AI workforce by analyzing 20,662 job postings on LinkedIn, including 72 prompt engineer positions, to learn more about this emerging role. We find 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. Our findings help job seekers, employers, and educational institutions in better understanding the emerging field of prompt engineering.

cs.CY↗

arXiv2025

An Exploration of Default Images in Text-to-Image Generation

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 over 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.

cs.HC↗

arXiv2025

Quo Vadis, HCOMP? A Review of 12 Years of Research at the Frontier of Human Computation and Crowdsourcing

The field of human computation and crowdsourcing has historically studied how tasks can be outsourced to humans. However, many tasks previously distributed to human crowds can today be completed by generative AI with human-level abilities, and concerns about crowdworkers increasingly using language models to complete tasks are surfacing. These developments undermine core premises of the field. In this paper, we examine the evolution of the Conference on Human Computation and Crowdsourcing (HCOMP) - a representative example of the field as one of its key venues - through the lens of Kuhn's paradigm shifts. We review 12 years of research at HCOMP, mapping the evolution of HCOMP's research topics and identifying significant shifts over time. Reflecting on the findings through the lens of Kuhn's paradigm shifts, we suggest that these shifts do not constitute a paradigm shift. Ultimately, our analysis of gradual topic shifts over time, combined with data on the evident overlap with related venues, contributes a data-driven perspective to the broader discussion about the future of HCOMP and the field as a whole.

cs.CY↗

arXiv2025

DangerMaps: Personalized Safety Advice for Travel in Urban Environments using a Retrieval-Augmented Language Model

Planning a trip into a potentially unsafe area is a difficult task. We conducted a formative study on travelers' information needs, finding that most of them turn to search engines for trip planning. Search engines, however, fail to provide easily interpretable results adapted to the context and personal information needs of a traveler. Large language models (LLMs) create new possibilities for providing personalized travel safety advice. To explore this idea, we developed DangerMaps, a mapping system that assists its users in researching the safety of an urban travel destination, whether it is pre-travel or on-location. DangerMaps plots safety ratings onto a map and provides explanations on demand. This late breaking work specifically emphasizes the challenges of designing real-world applications with large language models. We provide a detailed description of our approach to prompt design and highlight future areas of research.

cs.HC↗

arXiv2025

Keeping Score: A Quantitative Analysis of How the CHI Community Appreciates Its Milestones

The ACM CHI Conference has a tradition of citing its intellectual heritage. At the same time, we know CHI is highly diverse and evolving. In this highly dynamic context, it is not clear how the CHI community continues to appreciate its milestones (within and outside of CHI). We present an investigation into how the community's citations to milestones have evolved over 43 years of CHI Proceedings (1981-2024). Forgetting curves plotted for each year suggest that milestones are slowly fading from the CHI community's collective memory. However, the picture is more nuanced when we trace citations to the top-cited milestones over time. We identify three distinct types of milestones cited at CHI, a typology of milestone contributions, and define the Milestone Coefficient as a metric to assess the impact of milestone papers on a continuous scale. Further, we provide empirical evidence of a Matthew effect at CHI. We discuss the broader ramifications for the CHI community and the field of HCI.

cs.HC↗

arXiv2024

The Cultivated Practices of Text-to-Image Generation

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.

cs.CY↗

arXiv2024

Artworks Reimagined: Exploring Human-AI Co-Creation through Body Prompting

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.

cs.HC↗

arXiv2024

Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

We are witnessing a novel era of creativity where anyone can create digital content via prompt-based learning (known as prompt engineering). This paper investigates prompt engineering as a novel creative skill for creating AI art with text-to-image generation. In three consecutive studies, we explore whether crowdsourced participants can 1) discern prompt quality, 2) write prompts, and 3) refine prompts. We find that participants could evaluate prompt quality and crafted descriptive prompts, but they lacked style-specific vocabulary necessary for effective prompting. This is in line with our hypothesis that prompt engineering is a new type of skill that is non-intuitive and must first be acquired (e.g., through means of practice and learning) before it can be used. Our studies deepen our understanding of prompt engineering and chart future research directions. We conclude by envisioning four potential futures for prompt engineering.

cs.HC↗

arXiv2024

Past, Present, and Future of Citation Practices in HCI

Science is a complex system comprised of many scientists who individually make decisions that, due to the size and nature of the academic system, largely do not affect the system as a whole. However, certain decisions at the meso-level of research communities, such as the Human-Computer Interaction (HCI) community, may result in deep and long-lasting behavioral changes in scientists. In this article, we provide empirical evidence on how a change in editorial policies introduced at the ACM CHI Conference in 2016 destabilized the CHI research community and launched it on an expansive path, denoted by a year-by-year increase in the mean number of references included in CHI articles. If this near-linear trend continues undisrupted, an article at CHI 2030 will include on average almost 130 references. The trend toward more citations reflects a citation culture where quantity is prioritized over quality, contributing to both author and peer reviewer fatigue. Our exploratory analysis highlights the profound impact of meso-level policy adjustments on the evolution of scientific fields and disciplines, urging all stakeholders to carefully consider the broader implications of such changes.

cs.HC↗

arXiv2023

The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and Guidelines

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet they are often only mentioned in passing in the scholarly literature. A lack of details surrounding pilot studies in crowdsourcing research hinders the replication of studies and the reproduction of findings, stalling potential scientific advances. We conducted a systematic literature review on the current state of pilot study reporting at the intersection of crowdsourcing and HCI research. Our review of ten years of literature included 171 articles published in the proceedings of the Conference on Human Computation and Crowdsourcing (AAAI HCOMP) and the ACM Digital Library. We found that pilot studies in crowdsourcing research (i.e., crowd pilot studies) are often under-reported in the literature. Important details, such as the number of workers and rewards to workers, are often not reported. On the basis of our findings, we reflect on the current state of practice and formulate a set of best practice guidelines for reporting crowd pilot studies in crowdsourcing research. We also provide implications for the design of crowdsourcing platforms and make practical suggestions for supporting crowd pilot study reporting.

cs.HC↗

arXiv2023

Perceptions and Realities of Text-to-Image Generation

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.

cs.HC↗

arXiv2023

A Taxonomy of Prompt Modifiers for Text-To-Image Generation

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.

cs.MM↗

arXiv2023

Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scale

Large language models (LLMs) are increasingly used for analytical tasks, yet their effectiveness in real-world applications remains underexamined, partly due to the opacity of proprietary models. We evaluate ChatGPT (GPT-3.5 and GPT-4) on the practical task of extracting research challenges from a large scholarly corpus in Human-Computer Interaction (HCI). Using a two-step approach, we first apply GPT-3.5 to extract candidate challenges from the 879 papers in the 2023 ACM CHI Conference proceedings, then use GPT-4 to select the most relevant challenges per paper. This process yielded 4,392 research challenges across 113 topics, which we organized through topic modeling and present in an interactive visualization. We compare the identified challenges with previously established HCI grand challenges and the United Nations Sustainable Development Goals, finding both strong alignment in areas such as ethics and accessibility, and gaps in areas such as human-AI collaboration. A task-specific evaluation with human raters confirmed near-perfect agreement that the extracted statements represent plausible research challenges (\k{appa} = 0.97). The two-step approach proved cost-effective at approximately US$50 for the full corpus, suggesting that LLMs offer a practical means for qualitative text analysis at scale, particularly for prototyping research ideas and examining corpora from multiple analytical perspectives.

cs.HC↗

arXiv2023

Text-to-Image Generation: Perceptions and Realities

Generative AI is an emerging technology that will have a profound impact on society and individuals. Only a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on creative domains. In this paper, we present the key 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 ideas for potential application areas, as well as concerns, risks, and dangers of text-to-image generation to society and the individual. The study found that participants were aware of the risks and dangers associated with the technology, but only few participants considered the technology to be a risk to themselves. Additionally, those who had tried the technology rated its future importance lower than those who had not.

cs.HC↗

arXiv2023

Using Text-to-Image Generation for Architectural Design Ideation

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.

cs.HC↗