The dissemination of scholarly research is critical, yet researchers often lack the time and skills to create engaging content for popular media such as short-form videos. To address this gap, we explore the use of generative AI to help researchers transform their academic papers into accessible video content. Informed by a formative study with science communicators and content creators (N = 8), we designed PaperTok, an end-to-end system that automates the initial creative labor by generating script options and corresponding audiovisual content from a source paper. Researchers can then refine based on their preferences with further prompting. A mixed-methods user study (N = 18) and crowdsourced evaluation (N = 100) demonstrate that PaperTok’s workflow can help researchers create engaging and informative short-form videos. We also identified the need for more fine-grained controls in the creation process. To this end, we offer implications for future generative tools that support science outreach.

As LLM-based computer-use agents (CUAs) begin to autonomously interact with real-world interfaces, understanding their vulnerability to manipulative interface designs becomes increasingly critical. We introduce SusBench, an online benchmark for evaluating the susceptibility of CUAs to UI dark patterns, designs that aim to manipulate or deceive users into taking unintentional actions. Drawing nine common dark pattern types from existing taxonomies, we developed a method for constructing believable dark patterns on real-world consumer websites through code injections, and designed 313 evaluation tasks across 55 websites. Our study with 29 participants showed that humans perceived our dark pattern injections to be highly realistic, with the vast majority of participants not noticing that these had been injected by the research team. We evaluated five state-of-the-art CUAs on the benchmark. We found that both human participants and agents are particularly susceptible to the dark patterns of Preselection, Trick Wording, and Hidden Information, while being resilient to other overt dark patterns. Our findings inform the development of more trustworthy CUAs, their use as potential human proxies in evaluating deceptive designs, and the regulation of an online environment increasingly navigated by autonomous agents.

As conspiracy theories gain traction, it has become crucial to research effective intervention strategies that can foster evidence and science-based discussions in conspiracy theory communities online. This study presents a novel framework using insider language to contest conspiracy theory ideology in climate change denialism on Reddit. Focusing on discussions in two Reddit communities, our research investigates reactions to pro-social and evidence-based intervention messages for two cohorts of users: climate change deniers and climate change supporters. Specifically, we combine manual and generative AI-based methods to craft intervention messages and deploy the interventions as replies on Reddit posts and comments through transparently labeled bot accounts. On the one hand, we find that evidence-based interventions with neutral language foster positive engagement, encouraging open discussions among believers of climate change denialism. On the other, climate change supporters respond positively, actively participating and presenting additional evidence. Our study contributes valuable insights into the process and challenges of automatically delivering interventions in conspiracy theory communities on social media, and helps inform future research on social media interventions.

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

Incorporating uncertainty in data visualizations is critical for users to interpret and reliably draw informed conclusions from the underlying data. However, visualization creators conventionally convey the information regarding uncertainty in data visualizations using visual techniques (e.g., error bars), which disenfranchises screen-reader users, who may be blind or have low vision. In this preliminary exploration, we investigated ways to convey uncertainty in data visualizations to screen-reader users. Specifically, we conducted semi-structured interviews, finding that these users prefer to obtain statistical information on uncertainty expressed in plain language, conveyed holistically with avenues to explore the data further in a drilled-down manner. To support screen-reader users in extracting information about uncertainty in online data visualizations, we utilized our findings to enhance VoxLens—an open-source JavaScript plug-in that makes online data visualizations accessible to screen-reader users.

Makerspaces have complex access control requirements and are increasingly protected through digital access control mechanisms (e.g., keycards, transponders). However, it remains unclear how space administrators craft access control policies, how existing technical infrastructures support and fall short of access needs, and how these access control policies impact end-users in a makerspace. We bridge this gap through a mixed-methods, multi-stakeholder study. Specifically, we conducted 16 semi-structured interviews with makerspace administrators across the U.S. along with a survey of 48 makerspace end-users. We found four factors influenced administrators' construction of access control policies: balancing safety versus access; logistics; prior experience; and, the politics of funding. Moreover, administrators often made situational exceptions to their policies: e.g., during demand spikes, to maintain a good relationship with their staff, and if they trusted the user(s) requesting an exception. Conversely, users expressed frustration with the static nature of access control policies, wishing for negotiability and for social nuance to be factored into access decisions. The upshot is that existing mechanisms for access control in makerspaces are often inappropriately static and socially unaware.