Augmenting the ^Phonological Bridge^: The Role of AI-Powered Feedback in Singing Articulation for Early Speech Development
Berthanica Yohana, Rien Safrina, Elindra Yetti

Magister Pendidikan Seni, Universitas Negeri Jakarta, Indonesia

Abstract

Individualized speech support in diverse early childhood classrooms is a significant pedagogical challenge, often creating learning disparities. While effective, the scalability of arts-based interventions like the ^Phonological Bridge^ model is limited by an educator^s capacity for one-on-one feedback. This conceptual paper explores, through a literature review, how Artificial Intelligence (AI) can serve as a ^pedagogical amplifier^ to address this issue. Synthesizing research from computational linguistics, human-computer interaction, and arts-based pedagogy, we propose a model of human-AI collaboration. This framework posits that AI^s primary role is not to replace the educator, but to augment their capabilities. The literature suggests AI can automate pronunciation assessment, delivering personalized and immediate feedback to each child at scale. This process generates objective data that empowers teachers to shift from intuitive observation to data-informed intervention, freeing them to focus on higher-order tasks like fostering emotional connection and creativity. We conclude that this collaborative model represents a paradigm shift, recasting the teacher^s role from a sole instructor to a designer of enriched learning ecosystems. Its primary implication is the potential to democratize access to high-quality speech practice, promoting greater equity in foundational language skills for the AI era.

Keywords: Artificial Intelligence (AI), Human-AI Collaboration, Speech Development, Early Childhood Education, Singing Articulation, Educational Equity

Topic: AI in Language, Literature and Arts Education