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.

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.