Public Warning Systems (PWS) are critical infrastructures for protecting lives during emergencies, yet many users increasingly ignore or disable alerts. Prior research has focused on attentive recipients, overlooking those who disengage mentally or behaviorally. We examine disengagement as a gradual process of psychological detachment shaped by alert fatigue, trust erosion, and perceived inefficacy. Focusing on South Korea’s high-frequency cell broadcast system, averaging 80 messages per day, we conducted a qualitative study with 37 participants classified as responders, ignorers, or blockers, drawing on EPPM and PADM. Through interactive message evaluation and interviews, we traced cognitive and emotional pathways from message reception to protective action or inaction. Our findings reveal structural and psychological barriers, including fixed cognitive anchors that preemptively dismiss alerts, information-seeking behaviors rarely leading to action, and divergent adaptations to repeated false alarms. We reframe emergency alerts as adaptive user–system interfaces shaped by cumulative experience, not static channels. We show how PADM pathways become non-linear, truncated, or collapsed under saturated alert environments. We contribute design implications for more adaptive, trustworthy, and user-sensitive emergency alert systems.

While artificial intelligence (AI) is transforming UX design, its application often remains fragmented, focusing on isolated tasks like visualization rather than the cohesive research process. To address this gap, we present UREKA, a novel research assistant prototype designed to support the entire UX research workflow, from ideation and data analysis to planning and insight generation. We conducted a mixed-methods evaluation with 16 UX designers. The prototype achieved a System Usability Scale (SUS) score of 80.5, indicating excellent usability. Qualitative findings show participants praised UREKA’s ability to streamline research tasks and generate nuanced, project-specific personas. However, they also identified challenges, including the risk of generalized AI responses and an initial learning curve. Our findings demonstrate that an integrated AI tool like UREKA can significantly enhance research efficiency and support informed design decisions, highlighting a collaborative model where AI serves as a “cognitive accelerator” while the designer retains critical oversight.

Managing work quality has been an important issue for designing crowdsourcing tasks. Previous studies have proposed a number of ways to improve work quality, such as providing financial incentives, filtering random clickers, or designing workflow patterns. However, the potential benefit of having communication between the task owner and workers has been under-explored. This paper examined the effects of feedback and goal-setting messages on output quality in a crowdsourcing environment. The results revealed that negative and evaluative feedback and distal+proximal and achievement goal-setting messages improved output quality in shortening the volume, time, and cost to complete. The amount of monetary reward was found to have little to mixed influence over work quality, which meant more money did not lead to the higher work quality. Instead, the size of the reward must be paired with the appropriate type of messages for overall quality improvement.

Automated journalism refers to the generation of news articles using computer programs. Although it is widely used in practice, its user experience and interface design remain largely unexplored. To understand the user perception of an automated news system, we designed NewsRobot, a research prototype that automatically generated news on major events of the PyeongChang 2018 Winter Olympic Games in real-time. It produces six types of news by combining two kinds of content (general/individualized) and three styles (text, text+image, text+image+sound). A total of 30 users participated in using NewsRobot, completing surveys and interviews on their experience. Our findings are as follows: (1) Users preferred individualized news yet considered it less credible, (2) more presentation elements were appreciated but only if their quality was assured, and (3) NewsRobot was considered factual and accurate yet shallow in depth. Based on our findings, we discuss implications for designing automated journalism user interfaces.

Algorithms are playing an increasingly important role in the production of news content as their computation capacity in manipulating large-scale data continues to grow. In this article, we present Personalized and Interactive News Generation System (PINGS), an algorithm-driven news generation system that is designed to provide personalized and interactive news for sports. We designed PINGS to generate baseball news based on the statistical importance of data and the direct manipulation of user interface components that alter the underlying algorithmic computation. We discuss the base-level algorithm framework for automated news content generation and describe the architecture of the system in terms of how it is designed to support the generation of personalized news stories. An evaluation revealed that the algorithm is capable of generating news stories that are significantly more interesting and pleasant to read than traditional baseball news articles.