Successful job search results from job seekers' well-shaped social communication. While well-known diferences in communication exist between people with autism and neurotypicals, little is known about how people with autism collaborate with their social surroundings to strive in the job market. To better understand the practices and challenges of collaborative job seeking for people with autism, we interviewed 20 participants including applicants with autism, their social surroundings, and career experts. Through the interviews, we identified social challenges that people with autism face during their job seeking; the social support they leverage to be successful; and the technological limitations that hinder their collaboration. We designed four probes that represent major collaborative features found from the interviews-executive planning, communication, stage-wise preparation, and neurodivergent community formation-and discussed their potential usefulness and impact through three focus groups. We provide implications regarding how our findings can enhance collaborative job seeking experiences for people with autism through new designs.

Shadowing allows artists to convey realistic volume and emotion of characters in comic colorization. While AI technologies have the potential to improve professionals’ shadowing experience, current practice is manual and time-consuming. To understand how we can improve their shadowing experience, we conducted interviews with 5 professionals. We found that professionals’ level of engagement can vary depending on semantics, such as characters’ faces or hair. We also found they spent time on shadow “landscaping”—deciding where to put big shadow regions to make a realistic volumetric presentation—while the final results can dramatically vary depending on their “staging” and “attention guiding” needs. We found they would accept AI suggestions for less engaging semantic parts or landscaping, while they would need to have the capability to adjust details. Based on our observations, we built ShadowMagic that (1) generates AI-driven shadows based on typically used light directions, (2) enables a user to selectively choose the results depending on the semantics, and (3) allows users to finish shadow areas by themselves for further perfection. Through a summative evaluation with 5 professionals, we found that they were significantly more satisfied with our AI-driven results than a baseline. We also found ShadowMagic’s “step by step” workflow helps participants more easily adopt AI-driven results. We conclude by providing implications.