Public surveys are indispensable resources for understanding social dynamics, yet their analysis often imposes a high cognitive load due to structural complexity. In this paper, we present LAPS, a Large Language Model (LLM)-assisted automated framework that supports end-to-end, hypothesis-driven statistical analysis of survey data. LAPS consists of four modules (i.e., Operationalization, Planning, Execution, and Reporting) with human-in-the-loop mechanisms to balance automation with user agency. To evaluate the applicability of LAPS, we conducted a within-subjects user study with 12 social science researchers across three analytical environments: traditional statistical tools, a general-purpose LLM, and LAPS. Our findings demonstrate that LAPS ensures researcher agency and analytical stability, reduces the cognitive burden in the analysis workflow, and produces trustworthy, coherent outputs. Based on these findings, we reflect on how LAPS improves researchers’ workflows and discuss design implications for scalable and trustworthy human-AI collaboration in survey-based research.

Maintaining a daily routine has profound implications for physical, emotional, and social well-being. Autistic individuals may experience various challenges in establishing and maintaining a healthy daily routine due to their tendency to be inactive in daily life combined with their characteristics and preferences. Previous studies employing mobile technology to support autistic individuals have primarily focused on self-help functions, with limited exploration into the detailed needs of these individuals to develop and maintain personalized routines. In this study, we conducted a nine-week field study with 18 autistic individuals using RoutineAid, a gamified app designed to support key routines of autistic individuals (i.e., physical activity, diet, mindfulness, and sleep). Our analysis incorporated five measures of self-evaluation on daily life, app usage logs, Fitbit physical activity data, and interviews. Our findings demonstrate the effectiveness of RoutineAid and highlight its two primary affordances for autistic individuals: (1) promoting self-efficacy and embedding health behavior and (2) refining daily routines for healthier outcomes. We discuss salient design insights for developing daily routine management systems for autistic individuals.

Virtual reality (VR) has become a valuable tool for social and educational purposes for autistic people, as it provides flexible environmental support to create a variety of experiences. A growing body of recent research has examined the behaviors of autistic people using sensor-based data to better understand autistic people and investigate the effectiveness of VR. Comprehensive analysis of the various signals that can be easily collected in the VR environment can promote understanding of autistic people. While this quantitative evidence has the potential to help both autistic people and others (e.g., autism experts) to understand behaviors of autistic people, existing studies have focused on single signal analysis and have not determined the acceptability of signal analysis results from the autistic person’s point of view. To facilitate the use of multiple sensor signals in VR for autistic people and experts, we introduce V-DAT (Virtual Reality Data Analysis Tool), designed to support a VR sensor data handling pipeline. V-DAT takes into account four sensor modalities—head position and rotation, eye movement, audio, and physiological signals—that are actively used in current VR research for autistic people. We explain the characteristics and processing methods of the data for each modality as well as the analysis with comprehensive visualizations of V-DAT. We also conduct a case study to investigate the feasibility of V-DAT as a way of broadening understanding of autistic people from the perspectives of both autistic people and autism experts. Finally, we discuss issues with the process of V-DAT development and complementary measures for the applicability and scalability of a sensor data management system for autistic people.

Cybersickness is one of the problems that undermines user experience in virtual reality. While many studies are trying to find ways to alleviate cybersickness, only a few have considered cybersickness through multimodal perspectives. In this paper, we propose a multimodal, attention-based cybersickness prediction model. Our model was trained based on a total of 24,300 seconds of data from 27 participants and yielded the F1-score of 0.82. Our study results highlight the potential to model cybersickness from multimodal sensory information with a high level of performance and suggest that the model should be extended using additional, diverse samples.

Pervasive symptoms of people with autism spectrum disorder (ASD), such as a lack of social and communication skills, are major challenges to be embraced in the workplace. Although much research has proposed VR training programs, their effectiveness is somewhat unclear, since they provide limited, one-sided interactions through fixed scenarios or do not sufficiently reflect the characteristics of people with ASD (e.g., preference for predictable interfaces, sensory issues). In this paper, we present VISTA, a VR-based interactive social skill training system for people with ASD. We ran a user study with 10 people with ASD and 10 neurotypical people to evaluate user experience in VR training and to examine the characteristics of people with ASD based on their physical responses generated by sensor data. The results showed that ASD participants were highly engaged with VISTA and improved self-efficacy after experiencing VISTA. The two groups showed significant differences in sensor signals as the task complexity increased, which demonstrates the importance of considering task complexity in eliciting the characteristics of people with ASD in VR training. Our findings not only extend findings (e.g., low ROI ratio, EDA increase) in previous studies but also provide new insights (e.g., high utterance rate, large variation of pupil diameter), broadening our quantitative understanding of people with ASD.

As the number of dual-income household's increases, time spent by parents with children has been decreasing. To solve this issue, we have designed our system, Fam-On Platform. This platform is intended that it has increased the quantity of time by making parents recognized the lack of parenting time through time measurement. On the other hands, showing the amount of parenting time, it tries to decrease the imbalance of parenting time between fathers and mother as well. In this process, we manufactured physical wearable watch devices to promote children's interest and provide direct feedback to the family. Also, we tried to improve family bond by family-sharing time, providing contents to share among family members through application including gamification elements. We have completed a prototype of our platform and conducted pilot test targeting one child. We could gain the results we intended and find issues we need to improve further.