This paper describes the monumental shift in the nature of augmentative and alternative communication (AAC) access that has occurred over the past three decades. In its earliest days AAC technology was directed at enabling interpersonal face-to-face interactions mainly for persons with physical impairment. Contemporary AAC access attempts to mirror the access needs of a broader population. Accordingly AAC access to today's mainstream technologies expands the focus from interpersonal communication to access of information and services over the expanding World Wide Web. With this expanded view comes a new range of challenges and opportunities. At the same time AAC has expanded its reach to include more people with a wider range of complex communication needs.

Traditional topic-specific communication boards for Augmentative and Alternative Communication (AAC) require manual programming of relevant symbolic vocabulary, which is time-consuming and often impractical even for experienced Speech-Language Pathologists (SLPs). While recent research has demonstrated the potential to automatically generate these boards from photographs using artificial intelligence, there has been no exploration on how to design such tools to support the specific needs of AAC-based language instruction. This paper introduces QuickPic, a mobile AAC application co-designed with SLPs and special educators, aimed at enhancing language learning for non-speaking individuals, such as autistic children. Through a 17-month design process, we uncover the unique design features required to provide timely language support in therapy and special education contexts. We present emerging evidence on the overall satisfaction of SLPs using QuickPic, and on the advantages of large language model-based generation compared to the existing technique for automated vocabulary from photographs for AAC.

Jaswal & Akhtar provide several quotes ostensibly from people with autism but obtained via the discredited techniques of Facilitated Communication and the Rapid Prompting Method, and they do not acknowledge the use of these techniques. As a result, their argument is substantially less convincing than they assert, and the article lacks transparency.

As artificial intelligence (AI) makes significant headway in various arenas, the field of speech–language pathology is at the precipice of experiencing a transformative shift towards automation. This study introduces QuickPic AAC, an AI-driven application designed to generate topic-specific displays from photographs in a “just-in-time” manner. Using QuickPic AAC, this study aimed to (a) determine which of two AI algorithms (NLG-AAC and GPT-3.5) results in greater specificity of vocabulary (i.e., percentage of vocabulary kept/deleted by clinician relative to vocabulary generated by QuickPic AAC; percentage of vocabulary modified); and to (b) evaluate perceived usability of QuickPic AAC among practicing speech–language pathologists. Results revealed that the GPT-3.5 algorithm consistently resulted in greater specificity of vocabulary and that speech–language pathologists expressed high user satisfaction for the QuickPic AAC application. These results support continued study of the implementation of QuickPic AAC in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.