Autistic individuals often draw on insights from their supportive networks to develop self-help life strategies ranging from everyday chores to social activities. However, human resources may not always be immediately available. Recently emerging conversational agents (CAs) that leverage large language models (LLMs) have the potential to serve as powerful information-seeking tools, facilitating autistic individuals to tackle daily concerns independently. This study explored the opportunities and challenges of LLM-driven CAs in empowering autistic individuals through focus group interviews and workshops (N=14). We found that autistic individuals expected LLM-driven CAs to offer a non-judgmental space, encouraging them to approach day-to-day issues proactively. However, they raised issues regarding critically digesting the CA responses and disclosing their autistic characteristics. Based on these findings, we propose approaches that place autistic individuals at the center of shaping the meaning and role of LLM-driven CAs in their lives, while preserving their unique needs and characteristics.

Online dating is a space where autistic individuals can find romantic partners with reduced social demands. Autistic individuals are often expected to adapt their behaviors to the social norms underlying the online dating platform to appear as desirable romantic partners. However, given that their autistic traits can lead them to different expectations of dating, it is uncertain whether conforming their behaviors to the norm will guide them to the person they truly want. In this paper, we explored the perceptions and expectations of autistic adults in online dating through interviews and workshops. We found that autistic people desired to know whether they behaved according to the platform’s norms. Still, they expected to keep their unique characteristics rather than unconditionally conform to the norm. We conclude by providing suggestions for designing inclusive online dating experiences that could foster self-guided decisions of autistic users and embrace their unique characteristics.

Implementing structure into our daily lives is critical for maintaining health, productivity, and social and emotional well-being. New norms for routine management have emerged during the current pandemic, and in particular, individuals with autism find it difficult to adapt to those norms. While much research has focused on the use of computer technology to support individuals with autism, little is known about ways of helping them establish and maintain “self-directed” routine structures. In this paper, we identify design requirements for an app that support four key routine components (i.e., physical activity, diet, mindfulness, and sleep) through a formative study and develop RoutineAid, a gamified smartphone app that reflects the design requirements. The results of a two-month field study on design feasibility highlight two affordances of RoutineAid—the establishment of daily routines by facilitating micro-planning and the maintenance of daily routines through celebratory interactions. We discuss salient design considerations for the future development of daily routine management tools for individuals with autism.

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.

In recent years, detecting interactions between different genes has become a hot topic, for better understanding multigenic, complex diseases. For population-based genome-wide association studies (GWAS), a number of methods to detect gene-gene interactions such as logistic regression, multifactor dimensionality reduction (MDR) and support vector machine (SVM), have been applied. Bayesian approaches such as BEAM (Bayesian marker partition model) and DASSO-MB (detection of association using Markov Blanket) have also been suggested. However, the studies for family-based GWAS have been limited. In this study, we developed a new Markov Blanket-based algorithm called MB-TDT to find gene-gene interactions for pedigree data. A transmission disequilibrium test statistic was used as an association measure and the incremental association a Markov Blanket (IAMB) algorithm was applied to find Markov Blanket. This proposed MB-TDT method can identify a minimal set of causal SNPs, associated with a specific disease, thus avoiding an exhaustive search. By conducting a simulation study to compare MB-TDT with current methods, we show its superior high power in many cases, and lower false positive rates, in others.