AI-Based Technopreneurship for Speech Disorder Therapyin ADHD Children
While existing literature explores AI in healthcare, this study uniquely high-lights technopreneurial approaches to address accessibility and affordability ofAI-based therapies. Focusing on Indonesia as a case study, the paper exam-ines how AI can support children with ADHD who often face delays in speechdevelopment and communication. ADHD affects attention, language process-ing, and social interaction, creating challenges for effective therapy. AI inte-grates machine learning to analyze speech patterns, detect phonological errors,and provide adaptive therapy exercises tailored to children’s developmental lev-els. Findings indicate that AI improves diagnostic accuracy and delivers engag-ing interventions through interactive and gamified tools, enhancing motivationand participation for children who struggle with conventional therapy. AI sys-tems can track progress and adjust feedback in real time, offering personalizedsupport. However, barriers remain regarding affordability, infrastructure, andthe need for human oversight to manage complex emotional and behavioral re-sponses. In this context, technopreneurship is essential to scale affordable AI-based therapies for schools, clinics, and homes. By bridging gaps in healthcaredelivery, this study contributes to SDG 3 (Good Health), SDG 4 (Quality Ed-ucation), and SDG 10 (Reduced Inequalities). It emphasizes the importance ofinterdisciplinary collaboration among AI developers, speech-language patholo-gists, medical professionals, and educators. Overall, AI-driven technopreneur-ship demonstrates strong potential to improve early detection, therapy person-alization, and language development for children with ADHD, while ensuringbroader accessibility and sustainability.
Authors
- Subyantoro Subyantoro (ORCID: https://orcid.org/0000-0002-4365-5993)
- Yusro Edy Nugroho (ORCID: https://orcid.org/0000-0002-3437-0574)
- Ida Zulaeha (ORCID: https://orcid.org/0000-0001-7694-2895)
- Uki Hares Yulianti (ORCID: https://orcid.org/0009-0003-0498-6022)
- Kgomotso Moyo
Institutions
- State University of Semarang (ID)
Publication Details
- Journal
- Journal of Computer Science and Technology Application
- Published
- 2025-08-31
- DOI
- https://doi.org/10.33050/corisinta.v2i2.135
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00