An Adaptive Human-in-the-Loop Generative AI Framework for Professional-Supported Pronunciation Exercise Design: Ex Ante Evaluation in Down Syndrome

Personalized pronunciation-exercise design requires context-sensitive content, longitudinal adaptation, and professional oversight. This study presents Speak4All, a cloud-deployed human-in-the-loop (HITL) generative AI framework integrating structured learner profiles, professional assessments, interpretable difficulty recommendations, deterministic constraint validation, and explicit human control. Following a Design Science Research approach, an ex ante evaluation examined scenarios involving children with Down syndrome without recruiting children as participants. Two complementary settings were considered: an exploratory assessment with eight adult stakeholders (two speech-language professionals and six caregivers) in Ecuador and a remote controlled evaluation involving 25 education professionals and 25 structured cases. Compared with prompt-only and profile-aware generation, the integrated framework achieved higher educator ratings and 84.8% acceptance without editing across exercise-evaluation decisions. All retained exercises satisfied the specified hard constraints (100.0% post-validation compliance). Adaptive recommendations achieved 87.0% exact agreement with independent educator judgments (κw=0.848), with 85.0% accepted without modification. Stakeholder feedback indicated favorable perceptions of personalization, progress monitoring, and multimedia support. These findings support the framework’s ex ante technical feasibility and potential usefulness for professional-supervised educational decision support, without establishing clinical effectiveness. Future research should evaluate supervised use with children through validated speech-related outcome measures.