DreamGigs

Dillahunt, Tawanna R.; Lu, Alex Jiahong

doi:10.1145/3290605.3300808

Cited by 29 publications

(4 citation statements)

References 35 publications

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“…Dillahunt et al [18] proposed a framework to improve the design of current employment support tools for marginalized job seekers. Using these design requirements as a foundation, they developed a tool that helps job seekers to recognize their skill sets and find job openings that match with their skills [21]. Further, Dillahunt and Hsiao [19] conducted a field experiment on two tools.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities

Ara,

Ganguly,

Peppard

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Successful job search results from job seekers' well-shaped social communication. While well-known differences 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. CCS Concepts• Human-centered computing → Empirical studies in collaborative and social computing; Collaborative and social computing systems and tools; Empirical studies in accessibility; Accessibility systems and tools.

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Section: Related Workmentioning

confidence: 99%

Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities

Ara,

Ganguly,

Peppard

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…Past work in HCI has engaged with job seekers in acquiring technical skills [48] and identifying necessary skills to reach their dream jobs [20,34]. In particular, Suzuki et al designed a microinternship platform-Atelier-to help novice crowd workers develop the skills needed to deliver expert crowdsourcing tasks [48].…”

Section: Skills the Labor Market And Employmentmentioning

confidence: 99%

“…The platform paired novices and crowd experts to collaboratively break down a project into detailed milestones. In Dillahunt and Lu's work, they considered skills as the "stepping stone" and building blocks to help underrepresented job seekers to move to their dream jobs [20,34]. In particular, they implemented a system, DreamGigs, to help job seekers identify the skills that job seekers did not possess for their dream jobs and a path for job seekers to develop those skills.…”

Section: Skills the Labor Market And Employmentmentioning

confidence: 99%

“…While SkillsIdenfier served well to inform job seekers about their skills, it does not provide skills that job seekers need to develop for their target jobs. In fact, this need was addressed by another tool DreamGigs, which was developed by Dillahunt and Lu [20]. DreamGigs allowed job seekers to enter the "dream job" they want to apply for, and it output skills and training required to reach the job.…”

Section: Articulating Current Skills and Identifying Missing Skillsmentioning

confidence: 99%

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Elucidating Skills for Job Seekers: Insights and Critical Concerns from a Field Deployment in Switzerland

Cherubini

Hsiao

et al. 2021

Designing Interactive Systems Conference 2021

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This article contributes results of a longitudinal field study of Skill-sIdentifier, an employment tool originally designed and assessed in the United States (U.S.), to support "underrepresented" job seekers in identifying and articulating their employment skills. To understand whether the tool could support the needs of job seekers outside the U.S., we assessed it among 16 job seekers with limited education and language resources in Switzerland. While many of our results mirrored those of the U.S., we found that the tool was especially beneficial for non-French speaking immigrants who needed support describing their skills outside of their native language. We also found that listing skills like "active listening" without important context was insufficient and risked hiding key skills and meaning behind those skills to employers. Taking these factors into account, we illustrate the design implications of our findings and directions for practitioners who wish to design employment tools in support of job seekers, especially those who have traditionally been excluded from the labor market. We then provide insight into the potential for unintended consequences as a result of focusing solely on skills in a post-COVID labor market and contribute ways to mitigate them. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; User studies; • Social and professional topics → Employment issues.

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The March of Chatbots into Recruitment: Recruiters’ Experiences, Expectations, and Design Opportunities

Koivunen

Ala-Luopa

Olsson

et al. 2022

Comput Supported Coop Work

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Organizations’ hiring processes are increasingly shaped by various digital tools and e-recruitment systems. However, there is little understanding of the recruiters’ needs for and expectations towards new systems. This paper investigates recruitment chatbots as an emergent form of e-recruitment, offering a low-threshold channel for recruiter-applicant interaction. The rapid spread of chatbots and the casual nature of their user interfaces raise questions about the perceived benefits, risks, and suitable roles in this sensitive application area. To this end, we conducted 13 semi-structured interviews, including 11 interviews with people who are utilizing recruitment chatbots and two people from companies that are developing recruitment chatbots. The findings provide a qualitative account of their expectations and motivations, early experiences, and perceived opportunities regarding the current and future use of chatbots in recruitment. While chatbots answer the need for attracting new candidates, they have also introduced new challenges and work tasks for the recruiters. The paper offers considerations that can help to redesign recruitment bots from the recruiter’s viewpoint.

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DreamGigs

Cited by 29 publications

References 35 publications

Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities

Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities

Elucidating Skills for Job Seekers: Insights and Critical Concerns from a Field Deployment in Switzerland

The March of Chatbots into Recruitment: Recruiters’ Experiences, Expectations, and Design Opportunities

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