[Submitted on 20 Oct 2026]
Title:KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children
View a PDF of the paper titled KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children, by Maggie Dong and 5 other authors
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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.
Comments: Accepted to IEEE Spoken Language Technology 2026
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2610.28840 [cs.CL]
(or arXiv:2610.28840v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.28840
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Maggie Dong [view email] [v1] Tue, 20 Oct 2026 19:10:45 UTC (579 KB)
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