For high-variability populations, such as individuals with Autism Spectrum Disorders (ASD), the importance of personalization and adaptation mechanisms in Human-Robot Interaction becomes crucial. This technical report presents an algorithmic method for personalization of robotic behavior in structured social interactions with ASD children, based on state-of-the-art diagnostic models. In a first step, we leverage the structure of the diagnostic procedure to build robotic behaviors on a NAO humanoid robot, aimed at eliciting target behaviors from the child. Through appropriate sequencing of possible actions, the robot is able to assess a child’s behavioral profile and use it to personalize the interaction. To test our method, we developed a semi-autonomous robotic scenario where a humanoid robot interacts with a child with ASD through interactive storytelling, focusing on social prompts related to deficits in attention, one of the core impairments of ASD. We present the design and methodology of an evaluation study run with 11 young ASD children in a child development center in Portugal.