VACO: a Multi-perspective Development of a Therapeutic and Motivational Virtual Robotic Agent for Concentration for children with ADHD
<|reference_start|>VACO: a Multi-perspective Development of a Therapeutic and Motivational Virtual Robotic Agent for Concentration for children with ADHD: In this work, we present (i) a novel approach how artificial intelligence can support in the therapy for better concentration of children with Attention Deficit Hyperactivity Disorder (ADHD) through motivational attention training with a virtual robotic agent and (ii) a development process in which different stakeholders are included with their perspectives. Therefore, we present three participative approaches to include the perspectives of different stakeholders. An online survey (Study I) was conducted with parents in Germany with the aim of ascertaining whether they would use software to promote their children's attention, what influences their attitude towards using it, and what requirements it would have to meet. About half of the parents would be willing to use software to promote attention. To develop the software as close to practice as possible, one of the developers took part in an intensive training for ADHD with the aim of testing which of the elements are technically feasible. Afterward, a first prototype was presented to clinicians (Study II) to make further adjustments. A first feasibility test (Study III) was conducted with the end users to check if the system works and if children and adolescents can use it. Attentional performance software offers multiple opportunities in the treatment of ADHD if the system is adapted to the needs of the practitioner and end user. This development process requires a lot of time and close interdisciplinary collaboration.<|reference_end|>
@article{richter2024vaco:,
    title={VACO: a Multi-perspective Development of a Therapeutic and Motivational
  Virtual Robotic Agent for Concentration for children with ADHD},
    author={Birte Richter, Ira-Katharina Petras, Anna-Lisa Vollmer, Ayla Luong,
  Michael Siniatchkin, and Britta Wrede},
    journal={arXiv preprint arXiv:2405.03354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03354},
    primaryClass={cs.HC}
}