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Speech Enabled Reading Fluency Assessment: a Validation Study.

Max van der Velde^1,2, Wieke Harmsen³, Bernard P Veldkamp¹

¹Cognition, Data and Education Section, Faculty of BMS, University of Twente, De Zul 10, Enschede, NJ 7522 The Netherlands.

International Journal of Artificial Intelligence in Education

|December 11, 2025

Summary

This summary is machine-generated.

This study shows that automatic speech recognition (ASR) can accurately assess oral reading fluency in Dutch, a semi-transparent language. This AI-based tool offers a valid and efficient alternative for measuring reading skills in primary education.

Keywords:

Assessment Automatic Fluency Instrument Reading Validation

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Area of Science:

Educational Technology
Computational Linguistics
Psycholinguistics

Background:

Reading comprehension is crucial for societal function, yet student skills are declining globally.
Oral reading fluency (accuracy, speed, automaticity, prosody) is a prerequisite for reading comprehension.
Current fluency assessments are burdensome and lack diagnostic detail; AI offers potential solutions but requires validation.

Purpose of the Study:

To evaluate the validity of an automatic speech recognition (ASR)-based instrument for assessing oral reading fluency in a semi-transparent language.
To determine if ASR can generate valid measures of word decoding and passage reading (accuracy, speed, automaticity).

Main Methods:

A validation study using the Argument-Based Approach to Validation.
Analysis of 176 hours of speech data from 653 Dutch primary school children (grades 2-3).
Comparison with 569 word-reading and 622 passage-reading tests.

Main Results:

The ASR-based instrument successfully generated valid fluency metrics (accuracy, speed, automaticity) for Dutch.
The AI approach demonstrated feasibility for assessing reading fluency in semi-transparent languages.

Conclusions:

ASR-based assessment is a viable method for measuring oral reading fluency in semi-transparent languages.
Future work should focus on optimizing ASR, error analysis, and incorporating prosody for a comprehensive assessment tool.