Background: Receptive language, the ability to comprehend and respond to spoken language, poses significant challenges for individuals with Autism Spectrum Disorder (ASD). To support communication in autistic children, interventions like Lovaas' simple-conditional method and Green's conditional-only method are commonly employed. Personalized approaches are essential due to the spectrum nature of autism. Advancements in technology have opened new avenues for personalizing therapeutic interventions. This single case study compares traditional and technology-based learning sets in a receptive labeling teaching program using Green's method. Methods: An alternating treatments design assessed the number of sessions required to achieve mastery in receptive identification of stimuli presented on flashcards or tablets. The study involved a six-year-old Italian child with ASD named Pietro. Initial assessment using the Verbal Behavior Milestone Assessment and Placement Program (VB-MAPP) determined Pietro's strengths and weaknesses. Six stimuli were selected and divided into two sets: traditional and technology-based. Sessions were semi-randomly alternated, and the teaching procedures remained constant across conditions. In the traditional condition, sessions were conducted twice a week, using flashcards. Correct responses received immediate social reinforcement. In the technological condition, the same stimuli were presented on a tablet via PowerPoint slides. Results: Pietro achieved mastery more quickly with flashcard instruction than with tablet instruction. Learning was exponential in the traditional condition and linear in the digital condition. Follow-up assessments three weeks post-treatment showed no differences in the generalization and maintenance of skills between the two modalities. Discussion: The findings indicate that the format of stimulus delivery affects the learning process, with traditional flashcards leading to faster mastery in this case. Individual motivation appears crucial, suggesting that Pietro's learning history influenced his performance. Personalized approaches remain vital in autism interventions. Further research is needed to determine if these differences extend to other skills or contexts. Conclusion: While technology-based interventions offer new opportunities, they are not universally more effective than traditional methods. Careful consideration of individual differences, especially motivational factors, is essential in designing effective autism intervention programs.
