Project Summary/Abstract Children with speech sound disorder show diminished intelligibility in spoken communication, with negative con- sequences for both social-emotional and academic-occupational outcomes [1–4]. While most speech deviations resolve by the late school-age years, between 2-5% of speakers exhibit residual speech sound disorder (RSSD) that persists through adolescence or even adulthood [5–7]. Both affected children/families and speech-language pathologists (SLPs) have highlighted the critical need for research to identify more effective forms of treatment for children with RSSD. Our work in the previous funding cycle showed that individuals with RSSD benefit from treat- ment incorporating technologically enhanced sensory feedback (visual-acoustic biofeedback, ultrasound biofeed- back). However, real-world adoption of biofeedback treatment remains limited by equipment costs and lack of access to providers with specialized training. For our next phase of research, we focus on developing methods to support wider implementation of technology-enhanced treatment for RSSD. Specifically, we investigate the possibility that access to biofeedback can be expanded through telepractice service delivery and the use of Artificial Intelligence (AI)-powered technol- ogy to extend the services provided by SLPs. At the same time, we evaluate whether the efficacy of biofeedback can be further enhanced by adopting a precision medicine approach in which a treatment method is selected based on a learner’s individual profile of sensory strengths and weaknesses. In this proposal, we will conduct the first controlled comparison of biofeedback treatment delivered in person versus via telepractice (Aim 1), testing our hypothesis that biofeedback intervention can be delivered remotely without any unacceptable loss of efficacy. If successful, this study will greatly expand the reach of biofeedback by making it easier for children with RSSD to connect with trained clinicians. In Aim 2, we will utilize technology de- veloped in the previous funding cycle to assess whether the maintenance of gains achieved through biofeedback treatment can be enhanced through AI-mediated home practice. Finally, Aim 3 will lay groundwork for a precision medicine approach by testing whether relative response to ultrasound and visual-acoustic biofeedback can be predicted from a learner’s profile of sensory response across auditory and somatosensory domains. While new technologies have the potential to revolutionize the development and delivery of interventions for speech disorders, there is an ongoing need for well-designed research studies to bring these advances into evidence-based clinical practice. This research will address the needs of children with RSSD and the clinicians who treat them by providing a robust evidence base to guide clinical decision-making and user-friendly tools that support implementation.