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Background When transitioning from high school, autistic job seekers often navigate three different pathways to employment: University, Job Coaching, and Self-Directed (defined as those job seekers who independently complete the job search process, without formal support). Assistive technology may aid job seekers throughout the job seeking process. The aim of this study is to learn more about the challenges and assistive technology that autistic job seekers encounter while navigating these three different employment pathways. Methods Qualitative semi-structured interviews were conducted with fifteen stakeholders in the United States, autistic job seekers and support personnel, within each pathway of the hiring process to gather information regarding the challenges autistic job seekers encounter, and the assistive technology they use to address those challenges. Results From a thematic analysis of these interviews, we found that autistic job seekers along each pathway commonly move through the following, phases of the hiring process or “checkpoints”: resume building, networking, job search, job application, and interviews. Autistic job seekers also face challenges within each checkpoint, such as knowing when and what to disclose; self-efficacy, anxiety, and communication challenges; and a lack of communication from potential employers. We also learned that some self-directed autistic job seekers, when compared to those in the University and Job Coaching pathways, may not be using assistive technologies available in the job search process. From our interviews, we also learned the types of assistive technology that autistic job seekers and assistants use in the job seeking process which can be classified as organizational tools, connectivity tools, and visual media tools. Conclusion and implications Our findings revealed a necessity to connect self-directed autistic job seekers to assistive technology available. Based on these results, we present suggestions for future research and design suggestions for developing assistive technology for autistic job seekers. What this paper adds? We define three career pathways for autistic job seekers: University, Job Coaching and Self Directed. To learn more about the hiring process for autistic job seekers and the assistive technology used within each pathway, we conducted a need-finding study. As a contribution of this study, we discovered challenges along each checkpoint in the hiring process, as well as various forms of assistive technology used to support autistic job seekers when encountering those challenges. For our second contribution, we use the information from these interviews to provide suggestions for the design of future assistive technology within the hiring process, potentially supporting the self-efficacy of autistic job seekers, during this process. 

Abstract PurposeAn increasing number of autistic students in the United States are seeking post-secondary education. In response, some post-secondary institutions have established Autism Support Programs (ASP) to address the comprehensive needs of this population. There is little up-to-date, comprehensive information about which institutions host these programs, what types of services they offer, and what is required to access them. MethodsExpanding on previous research, we introduce a new method, which utilizes established data science techniques, to identify ASPs at post-secondary institutions in the U.S. Our technique also allows us to identify the characteristics of the ASPs, including admissions requirements, cost, structure, and supports offered. ResultsResults highlight our method is more efficient and more robust than previous methods from the literature. For example, we identify 49 schools hosting ASPs that were not identified in past literature searches. We report on the characteristics of identified ASPs such as application process, most common supports and program cost. ConclusionThe bi-directional change in the number of ASPs shows that this is an evolving field, requiring automated tools to enable regular updates to data. Although it is promising that a relative handful of U.S. schools have established these programs, a large majority of post-secondary institutions have not, and for those that host them, barriers to access exist, including the necessity of an ASD diagnosis, coupled with up-front and ongoing costs. 

Autistic children face significant challenges in vocal communication and social interaction, often leading to social isolation. There is evidence that Augmentative and Alternative Communication (AAC) offers support to mitigate these challenges, enabling them to communicate with non-vocal means through forms of AAC, such as speech-generation devices (SGDs). However, the adoption and use of SGDs are hindered by several factors, including the large amount of practice required to learn to use SGDs and the limited options for highly engaging social learning contexts. Our study introduces the novel approach of using SGDs as game controller for digital and interactive games. With three design goals guiding our work, we conducted a Wizard-of-Oz formative case study with five participants aged 3-5 years, who were learning to use their SGD. We simulated a digital coloring game, integrating the speech-generated output of the participant's SGD to function as the game's controller. From this case study, we observed that all participants engaged with the game using their SGD for at least one turn, and two participants also engaged in emerging joint attention responses with the game and game's facilitator. This paper discusses these findings and contributes directions for future research, with suggestions for the design of future SGD-controlled games and exploration of social connection and collaboration between autistic children who use AAC and their caregivers, siblings, and peers. 

Abstract Millions of individuals who have limited or no functional speech use augmentative and alternative communication (AAC) technology to participate in daily life and exercise the human right to communication. While advances in AAC technology lag significantly behind those in other technology sectors, mainstream technology innovations such as artificial intelligence (AI) present potential for the future of AAC. However, a new future of AAC will only be as effective as it is responsive to the needs and dreams of the people who rely upon it every day. AAC innovation must reflect an iterative, collaborative process with AAC users. To do this, we worked collaboratively with AAC users to complete participatory qualitative research about AAC innovation through AI. We interviewed 13 AAC users regarding (1) their current AAC engagement; (2) the barriers they experience in using AAC; (3) their dreams regarding future AAC development; and (4) reflections on potential AAC innovations. To analyze these data, a rapid research evaluation and appraisal was used. Within this article, the themes that emerged during interviews and their implications for future AAC development will be discussed. Strengths, barriers, and considerations for participatory design will also be described. 

Introduction: Social participation for emerging symbolic communicators on the autism spectrum is often restricted. This is due in part to the time and effort required for both children and partners to use traditional augmentative and alternative communication (AAC) technologies during fast-paced social routines. Innovations in artificial intelligence provide the potential for context-aware AAC technology that can provide just-in-time communication options based on linguistic input from partners to minimize the time and effort needed to use AAC technologies for social participation. Methods: This preliminary study used an alternating treatment design to compare the effects of a context-aware AAC prototype with automated cloze phrase response options to traditional AAC for supporting three young children who were emerging symbolic communicators on the autism spectrum in participating within a social routine. Results: Visual analysis and effect size estimates suggest the context-aware AAC condition resulted in increases in linguistic participation, vocal approximations, and visual attention for all three children. Conclusion: While this study was only an initial exploration and results are preliminary, context-aware AAC technologies have the potential to enhance participation and communication outcomes for young emerging symbolic communicators on the autism spectrum and more research is needed. 

Purpose:Augmentative and alternative communication (AAC) technology innovation is urgently needed to improve outcomes for children on the autism spectrum who are minimally verbal. One potential technology innovation is applying artificial intelligence (AI) to automate strategies such as augmented input to increase language learning opportunities while mitigating communication partner time and learning barriers. Innovation in AAC research and design methodology is also needed to empirically explore this and other applications of AI to AAC. The purpose of this report was to describe (a) the development of an AAC prototype using a design methodology new to AAC research and (b) a preliminary investigation of the efficacy of this potential new AAC capability. Method:The prototype was developed using a Wizard-of-Oz prototyping approach that allows for initial exploration of a new technology capability without the time and effort required for full-scale development. The preliminary investigation with three children on the autism spectrum who were minimally verbal used an adapted alternating treatment design to compare the effects of a Wizard-of-Oz prototype that provided automated augmented input (i.e., pairing color photos with speech) to a standard topic display (i.e., a grid display with line drawings) on visual attention, linguistic participation, and (for one participant) word learning during a circle activity. Results:Preliminary investigation results were variable, but overall participants increased visual attention and linguistic participation when using the prototype. Conclusions:Wizard-of-Oz prototyping could be a valuable approach to spur much needed innovation in AAC. Further research into efficacy, reliability, validity, and attitudes is required to more comprehensively evaluate the use of AI to automate augmented input in AAC. 

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. 

Abstract Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general.