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Handwashing is a vital skill for maintaining health and hygiene. For individuals with intellectual and developmental disabilities (IDD), such as autism spectrum disorder, evidence-based strategies, such as prompting and task analysis, may be effective in teaching these skills. Due to the shortage of experts who teach individuals with IDD skills such as handwashing, staff working with children need a means of ensuring these instructional strategies are implemented with fidelity. This study examined the effects of a tablet-based application that used artificial intelligence (GAINS®) on four behavior technicians' implementation of least-to-most prompting, total task chaining, and time delay during an acquisition of handwashing program with young children with autism. All four technicians increased fidelity immediately upon using GAINS and all four technicians reached mastery criteria within the shortest number of sessions possible. One child participant met mastery criteria, two showed some gains, and one demonstrated a high degree of variability across sessions. Limitations of the least-to-most prompting procedure, user design, considerations and directions for future research and practice are discussed. [ABSTRACT FROM AUTHOR]

Individuals with intellectual and developmental disabilities (IDD) face many barriers to meaningful inclusion, including limited language and communication skills. Professionals, such as speech-language pathologists (SLPs), can provide personalized instruction to promote skill development and inclusion. Providing opportunities for individuals to express preferences and choice, such as the multiple stimulus without replacement preference assessment (MSWO; DeLeon & Iwata 1996), within these programs, further increases skill acquisition and social interaction. However, limitations in professionals’ knowledge and skills in performing assessments can be another barrier to meaningful inclusion for individuals with IDD and traditional training methods can be challenging and time consuming. The purpose of the current study was to compare the use of artificial intelligence with traditional pen and paper self-instructional MSWO training methods for five preservice SLPs. Fidelity of implementation and duration of assessment were measured. Results demonstrated a large increase in implementation fidelity for two participants, a moderate increase for two participants and a slight increase for the remaining participant while using artificial intelligence. All participants demonstrated a decrease in scoring errors using artificial intelligence. Regarding duration of implementation, artificial intelligence resulted in a significant reduction for four participants and a moderate reduction for the remaining participant. Results of the follow-up survey suggest that all adult participants and both child participants found that artificial intelligence had a higher treatment acceptability and was more effective at producing socially significant outcomes than traditional methods. Recommendations for clinicians and future research are discussed. [ABSTRACT FROM AUTHOR]