Evaluating the scoring system of an AI-integrated app to assess foreign language phonological decoding

Abstract

Phonological decoding in a foreign language (FL)—a two-part process involving first the ability to map written symbols to their corresponding sounds and second to pronounce them intelligibly—is foundational for reading and vocabulary acquisition. Yet assessing this skill efficiently and at scale in young learners remains a persistent challenge. Here, we introduce and evaluate the accuracy and effectiveness of a novel method for assessing FL phonological decoding using an AI-driven app that automatically scores children's pronunciation of symbol-sound correspondences. In a study involving 254 learners of French and Spanish (aged 10–11) across five UK primary schools, pupils completed a read-aloud task (14 symbol-sound correspondences) that was scored by the app’s automatic speech recognition (ASR) technology. The validity of these automated scores was tested by fitting them as independent variables in regression models predicting human auditory coding. The multiple significant relationships between automated and human scores that were established indicate that there is great potential for ASR-based tools to reliably assess phonological decoding in this population. These findings provide the first large-scale empirical validation of an AI-based assessment of FL decoding in children, opening new possibilities, applicable to a range of languages being learnt, for scalable and efficient assessment.

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Identifiers

ORCID for James Turner: orcid.org/0000-0001-6724-3056

ORCID for Alison Porter: orcid.org/0000-0002-8462-1909

ORCID for Kate Borthwick: orcid.org/0000-0003-2251-7898

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