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

Maggie Dong; Rohit Mekkoth; Zixuan Wang; Cheol Jun Cho; Jiachen Lian; Gopala Anumanchipalli

cs.CLOct 20, 2026

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

Authors: Maggie Dong, Rohit Mekkoth, Zixuan Wang, Cheol Jun Cho, Jiachen Lian, Gopala Anumanchipalli

Organizations: Dept. of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA

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.

Educational Assessment

Educational assessment measures what learners know and can do, using tests, rubrics, and learning evidence to guide decisions.

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Building an ASR Solution for Training and Assessing Children's Reading

Yacouba Diarra, Nouhoum Souleymane Coulibaly, Mamadou Dembele, Aymane Dembele+1

RobotsMali AI4D Laboratory Bamako, Mali

Child Speech Recognition Automatic Speech Recognition Low-Resource Speech Recognition

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Automatic speech recognition for children's reading remains underdeveloped for most African languages, including Bambara, despite its potential value for reproducible literacy assessment. We present an open-source system for assessing children's reading in Bambara, developed through an end-to-end process linking field data collection, benchmark construction, model adaptation, a reading application, and classroom validation. A mobile collection and assessment app was used to collect 55 hours of raw reading speech from 60 children, from which we construct a public benchmark for Bambara child-reading assessment. Fine-tuning experiments compare Soloni, a Bambara-adapted Fast-Conformer ASR framework with TDT and CTC decoders, with QuartzNet, a compact convolutional ASR architecture. The best Soloni model reduces WER from 0.42 to 0.22 and CER from 0.15 to 0.08, substantially outperforming QuartzNet on the isolated benchmark. The experiments further show that repeated readings of the same texts provide architecture-dependent benefits: they substantially improve QuartzNet but add only marginal gains for Soloni, while SpecAugment regulates training without exceeding the best unaugmented configuration. Disaggregated analysis identifies children under 10 as the main source of residual errors, motivating targeted collection from younger readers. Ten classroom trials supported continued use of the application.

Abstract

Educational Assessment

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