Keywords
Mobile health applications
Intelligent tutoring systems
Pedagogical design
Metrica
How to Cite
Abstract
This study systematically maps AI-based speech therapy applications and analyses their pedagogical design. Focusing on end-user mobile apps available in major app stores, we included twenty-five applications that explicitly combined speech, language, or communication therapy with artificial intelligence or automated speech analysis. Each app was coded using a framework informed by speech-language therapy literature, intelligent tutoring systems, and mobile health app evaluation, capturing AI adaptivity, feedback immediacy, multimodal feedback, evidence basis, pedagogical alignment, gamification, stakeholder dashboards, pricing, user rating, and an adoption proxy. Descriptive statistics, Pearson and Spearman correlations, and thematic analysis were applied at the app level. The mapping shows moderate AI adaptivity and rapid feedback in most apps, with substantial variation in evidence basis and pedagogical alignment. Evidence basis and pedagogical alignment were strongly correlated (r ≈ 0.82) and both were positively associated with user ratings and estimated active users, while higher subscription cost showed a moderate negative association with adoption. Thematic analysis revealed recurring design patterns such as gamified home practice, limited clinician-in-the-loop features, uneven data privacy transparency, and partial uptake of multilingual and offline support. The findings highlight a gap between rapidly advancing AI capabilities and consistently principled pedagogical design in commercial speech therapy apps.
References
[1] A. A. Macedo, V. de S. Gonçalves, P. P. Mandrá, V. Motti, R. F. Bulcão-Neto, and K. R. da Hora Rodrigues, "A mobile application and system architecture for online speech training in Portuguese: Design, development, and evaluation of SofiaFala," Multimedia Tools and Applications, vol. 84, no. 20, pp. 22625–22654, 2025.
[2] R. G. Barnett and P. W. Cascella, "A qualitative evaluation of treatment app features for children with speech sound disorders," Communication Disorders Quarterly, vol. 47, no. 1, pp. 28–35, 2025.
[3] S. Saeedi, M. Ghazisaeedi, N. Ramezanghorbani, M.-S. Seifpanahi, and H. Bouraghi, "Design and evaluation of a serious video game to treat preschool children with speech sound disorders," Scientific Reports, vol. 14, no. 1, p. 17299, 2024.
[4] N. Leafe, E. Pagnamenta, L. Taggart, M. Donnelly, A. Hassiotis, and J. Titterington, "What works, how and in which contexts when using digital health to support parents/carers to implement intensive speech and language therapy at home for children with speech sound disorder? A realist review," PLOS ONE, vol. 20, no. 5, p. e0321647, 2025.
[5] C. Deka, A. Shrivastava, A. K. Abraham, S. Nautiyal, and P. Chauhan, "AI-based automated speech therapy tools for persons with speech sound disorder: A systematic literature review," Speech, Language and Hearing, vol. 28, no. 1, p. 2359274, 2025.
[6] H. Suh, A. Dangol, H. Meadan, C. A. Miller, and J. A. Kientz, "Opportunities and challenges for AI-based support for speech-language pathologists," in Proc. 3rd Annu. Meeting Symp. Human-Computer Interaction for Work, pp. 1–14, 2024.
[7] A. Dangol, A. Lewis, H. Suh, X. Hong, H. Meadan, J. Fogarty, and J. A. Kientz, "“I want to think like an SLP”: A design exploration of AI-supported home practice in speech therapy," in Proc. 2025 CHI Conf. Human Factors in Computing Systems, pp. 1–22, 2025.
[8] G. Gallano, A. Giglio, and A. Ferre, "Artificial intelligence in speech-language pathology and dysphagia: A review from Latin American perspective and pilot test of LLMs for rehabilitation planning," Journal of Voice, 2025.
[9] Q. Ain and R. Imtiaz, "The role of technology in speech-language therapy: Perceptions, effectiveness, and challenges in a resource-limited setting," Journal of Health and Rehabilitation Research, vol. 5, no. 1, pp. 1–8, 2025.
[10] A. Bhardwaj, M. Sharma, S. Kumar, S. Sharma, and P. C. Sharma, "Transforming pediatric speech and language disorder diagnosis and therapy: The evolving role of artificial intelligence," Health Sciences Review, vol. 12, p. 100188, 2024.
[11] G. P. Georgiou, "Transforming speech-language pathology with AI: Opportunities, challenges, and ethical guidelines," in Healthcare, vol. 13, no. 19, p. 2460, 2025.
[12] N. R. Benway and J. L. Preston, "Equipping speech-language clinicians for the critical appraisal of an artificial intelligence--driven, evidence-based future," Language, Speech, and Hearing Services in Schools, pp. 1–27, 2025.
[13] M. Mahmoudi-Dehaki and N. Nasr-Esfahani, "Artificial intelligence (AI) in special education: AI therapeutic pedagogy for language disorders," in Transforming Special Education Through Artificial Intelligence, pp. 193–222, 2025.
[14] S. M. ElHennawy, "The impact of artificial intelligence (AI) in the assessment and treatment of communication disorders (a review of literature)," The Egyptian Journal of Language Engineering, vol. 11, no. 2, pp. 36–45, 2024.
[15] S. Dada, C. Flores, K. Bastable, K. Tönsing, A. Samuels, S. Mukhopadhyay, B. Isanda, J. O. Bampoe, U. Stemela-Zali, S. B. Karim et al., "Use of an intelligent tutoring system for a curriculum on augmentative and alternative communication: Feasibility for implementation," International Journal of Language & Communication Disorders, vol. 59, no. 6, pp. 2279–2293, 2024.
[16] K. Kanwal, Z. Ibrahim, and A. A. Siddiqui, "The convergence of artificial intelligence, digital therapeutics, and biomedical electronics in the field of speech-language therapy: AI and digital innovations in speech therapy," Allied Medical Research Journal, vol. 2, no. 2, pp. 277–292, 2024.
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