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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. 

Augmentative and alternative communication (AAC) devices are used by many people around the world who experience difficulties in communicating verbally. One form of AAC device which is especially useful for minimally verbal autistic children in developing language and communication skills is the visual scene display (VSD). VSDs use images with interactive hotspots embedded in them to directly connect language to real-world contexts which are meaningful to the AAC user. While VSDs can effectively support emergent communicators (i.e., those who are beginning to learn how to use symbolic communication), their widespread adoption is impacted by how difficult these devices are to configure. We developed a prototype that uses generative AI to automatically suggest initial hotspots on an image to help non-experts efficiently create visual scene displays (VSDs). We conducted a within-subjects user study to understand how effective our prototype is in supporting non-expert users, specifically pre-service speech-language pathologists (SLPs) (N=16) who are not familiar with VSDs as an AAC intervention. Pre-service SLPs are actively studying to become clinically certified SLPs and have domain-specific knowledge about language and communication skill development. We evaluated the effectiveness of our prototype based on creation time, quality, and user confidence. We also analyzed the relevance and developmental appropriateness of the automatically generated hotspots and how often users interacted with (e.g., editing or deleting) the generated hotspots. Our results were mixed with SLPs becoming more efficient and confident. However, there were multiple negative impacts as well, including over-reliance and homogenization of communication options. The implications of these findings reach beyond the domain of AAC, especially as generative AI becomes more prevalent across domains, including assistive technology. Future work is needed to further identify and address these risks associated with integrating generative AI into assistive technology. 

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. 

Grid displays are the most common form of augmentative and alternative communication device recommended by speech-language pathologists for children. Grid displays present a large variety of vocabulary which can be beneficial for a users’ language development. However, the extensive navigation and cognitive overhead required of users of grid displays can negatively impact users’ ability to actively participate in social interactions, which is an important factor of their language development. We present a novel interaction technique for grid displays, Predictive Anchoring, based on user interaction theory and language development theory. Our design is informed by existing literature in AAC research, presented in the form of a set of design goals and a preliminary design sketch. Future work in user studies and interaction design are also discussed. 

Not AvailableMany conversational user interfaces facilitate linear conversations with turn-based dialogue, similar to face-to-face conversations between people. However, digital conversations can afford more than simple back-and-forth; they can be layered with interaction techniques and structured representations that scaffold exploration, reflection, and shared understanding between users and AI systems. We introduce Feedstack, a speculative interface that augments feedback conversations with layered affordances for organizing, navigating, and externalizing feedback. These layered structures serve as a shared representation of the conversation that can surface user intent and reveal underlying design principles. This work represents an early exploration of this vision using a research-through-design approach. We describe system features and design rationale, and present insights from two formative (n=8, n=8) studies to examine how novice designers engage with these layered supports. Rather than presenting a conclusive evaluation, we reflect on Feedstack as a design probe that opens up new directions for conversational feedback systems. 

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.