Integrating AI into ADHD Therapy: Insights from ChatGPT-4o and Robotic Assistants

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Abstract

Traditional attention-deficit/hyperactivity disorder (ADHD) interventions often struggle with scalability, requiring significant human resources to ensure individualized attention and real-time adaptability, as many existing tools rely on fixed content that cannot adjust to fluctuating user behaviors. Additionally, most current methods lack multi-modal interaction, limiting their ability to engage children effectively across speech, text, and visual feedback. This study explores the integration of ChatGPT-4o with robotic assistants to enhance personalization, engagement, and adaptability in ADHD therapy. Unlike pre-scripted or static therapy tools, ChatGPT-4o enables dynamic, real-time interactions tailored to individual needs, providing a more engaging and adaptable therapeutic experience. The results validate its potential to deliver personalized, scalable, and responsive therapeutic support through AI-driven robotic assistants. Technical validation demonstrated inclusivity across diverse user profiles, achieving a 93% success rate in tailored interactions and maintaining stability in 91% of extreme input scenarios. Cross-platform tests confirmed high accuracy, while scalability assessments validated effective handling of multi-user interactions. Clinical evaluations over eight weeks indicated potential improvements in attention span (+ 28%) and reductions in hyperactivity (− 22%), with high satisfaction ratings from therapists, caregivers, and teachers. These results establish ChatGPT as a promising tool for ADHD therapy, enhancing engagement, personalization, and scalability. Future directions include optimizing for younger users, deploying to real-world environments, and further interdisciplinary advancements to refine AI-driven therapeutic solutions.

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APA

Berrezueta-Guzman, S., Kandil, M., & Wagner, S. (2025). Integrating AI into ADHD Therapy: Insights from ChatGPT-4o and Robotic Assistants. Human-Centric Intelligent Systems, 5(2), 230–245. https://doi.org/10.1007/s44230-025-00099-1

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