KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children

Researchers behind the arXiv paper 2610.28840v1 released KIRA, a dual-pathway reading screening system that adapts pretrained word and phoneme recognizers to children's speech, cutting word error rate from 13.88% to 8.84% and phoneme error rate from 41.66% to 23.00%. KIRA pairs word-level fluency scoring with phoneme-level error detection to support Word Reading (WRE) and Oral Reading Fluency (ORF), two foundational K-2 screening tasks, and delivers results through a web-based teacher-facing interface that shows fluency scores alongside phoneme feedback for targeted intervention. The system is presented as a full-stack, deployable, classroom-ready screener, addressing the limited scope and opaque evaluation of existing proprietary AI screeners.

arXiv:2610.28840v1 Announce Type: new Abstract: Early-grade reading screening is critical for identifying children at risk of reading difficulty. Existing AI-based screeners are often limited in the scope of their analyses and evaluation metrics, or are proprietary systems that lack transparent model specifications and evaluation. We present KIRA, a dual-pathway screening system that pairs word-level fluency scoring with detailed phoneme-level error detection. We adapt pretrained word and phoneme recognizers to children's speech, reducing word error rate from 13.88% to 8.84% and phoneme error rate from 41.66% to 23.00%. Powered by these child-adapted models, we implement Word Reading WRE and Oral Reading Fluency ORF , two foundational tasks in K-2 screening. The system integrates these recognizers with a web-based teacher-facing interface presenting fluency scores alongside phoneme feedback for potential targeted intervention. KIRA brings modern child-speech recognition into a full-stack, deployable, classroom-ready reading screener.