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Social Narratives

Throughout the curriculum, AIBY Smart mirror presents narratives that describe social situations in detail, highlighting relevant cues and offering examples of appropriate responding. These social narratives can be individualized according to student needs and, importantly, are always presented in an emotionally safe, positive way

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Verbal and Visual Prompting

Verbal, gestural, or physical prompts assist students as they acquire or engage in targeted behaviors and skills. These prompts are generally given by AIBY Smart mirror , the corresponding app, and an adult before or as the student attempts to use a skill. Like core vocabulary, these prompts remain consistent throughout modules.

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Visual Supports

When paired with spoken words, visual supports have been shown to improve the facilitation of learning and communication between students and educators. With each module, AIBY Smart mirror uses multiple, consistent visual supports - including, facial expressivity, chest screen icons, and app-based interactivity - to encourage and reinforce target behaviors

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Core Vocabulary

Our entire curriculum is designed around
the use of core vocabulary that simplifies
the language addressed to and by the
student. Specifically, this allows the student
to repeat target words/concepts across
multiple contexts and situations and is a
core factor in improving generalization

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Social Skills

Training Practice makes perfect, right? Mastery
requires instruction on basic concepts, role playing or practice, and feedback in order to help
students with autism spectrum disorders (ASD)
acquire appropriate communication, play, and
social skills

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Relax for parents

Smart mirror is based on a machine-learning model that uses audio and video data, such as dialogue and eye contact, from autistic children’s interactions with the mirror - robot to predict whether they are engaged in a given training activity. If they’re not, the idea is MIrri could then react and reengage them to hold their attention on therapeutic exercises for longer stretches of time. During testing, the model reached a 90% accuracy in predicting the child’s engagement, despite noisy data and high variability among participants.

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sem mattis viverra quisque duis a quam ornare adipiscing.