Functional Communication and Artificial Intelligence: Results of a Training in a Student with Dravet Syndrome

Abstract

Dravet Syndrome is a severe epileptic disorder that significantly impairs communication. Despite the growing interest in technological aids, few studies have investigated the use of artificial intelligence (AI) to support Functional Communication Training (FCT) in this population. The present study explores the integration of AI‐based tools to enhance communicative autonomy and reduce maladaptive behaviors in a child with Dravet Syndrome. A single‐case experimental design was adopted with a nine‐year‐old participant. The intervention combined FCT with the use of Google Assistant as an AI‐mediated reinforcement delivery system. The child was required to ask for reinforcers via voice commands, gaining immediate access to preferred stimuli following successful communicative attempts. Data on communicative attempts were collected at the pre‐test, training and post‐test stages. Results showed an increase in the participant's ability to request reinforcers independently, with a shift from variable performance during baseline to 100% accuracy in the last training session. Post‐test data indicated stable generalization of target behavior, confirming the acquisition of functional communication skills.
The study suggests that AI can be effectively integrated into reinforcement‐based interventions such as FCT, offering a viable and low‐cost solution to support communicative development in individuals with severe neurodevelopmental disorders. Limitations include device preference, network reliability, and environmental variables that may influence outcomes.