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.

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.

The objective of this case report is to demonstrate how a dynamic assessment approach may be utilized to identify appropriate augmentative and alternative communication (AAC) tools to support the expressive and receptive language skills among individuals with autism spectrum disorder (ASD) who are minimally verbal. This case report details an AAC assessment for Jack, a four-year-old boy diagnosed with ASD and a mixed receptive-expressive language disorder. Specific strategies from the Visual Immersion System™ (VIS™), which is a treatment approach that utilizes the visual strengths of individuals with ASD, were identified and incorporated into Jack’s treatment. Jack’s case highlights the need to use a thorough, individualized assessment approach that includes identifying the level of symbolic representation prior to selecting appropriate AAC supports to facilitate continued development of receptive and expressive language for individuals with ASD.

A substantial portion of individuals with autism spectrum disorder (ASD) are nonspeaking, requiring alternative and augmentative communication (AAC) to convey wants and needs with others. With many options available to consumers, AAC systems are often selected without carefully matching the needs of the communicator with the appropriate AAC features. This chapter details the use of the feature-matching process to obtain a high-tech AAC device for a young child with ASD who was using his previous AAC system solely to request. The feature-matching process entailed a multidisciplinary evaluation to select an AAC system that best fit his current and future communication needs, and completion of an evidence-based trial to ensure appropriateness of the device. As a result of this process, the child became a more effective communicator by replacing maladaptive behaviors with functional communication, broadening the purposes for which he communicated, and expanding his circle of communication partners.

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.