Can Artificial Intelligence Identify Reading Fluency and Level? Comparison of Human and Machine Performance

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dc.authoridAydoğmuş, Mücahit/0000-0002-1418-1100en_US
dc.authoridoyucu, saadin/0000-0003-3880-3039en_US
dc.authorscopusid36350521600en_US
dc.authorscopusid55995590700en_US
dc.authorscopusid57194831938en_US
dc.authorscopusid24402553200en_US
dc.authorscopusid57358071400en_US
dc.authorscopusid59013657900en_US
dc.authorwosidAydoğmuş, Mücahit/ABI-5187-2020en_US
dc.contributor.authorYildiz, Mustafa
dc.contributor.authorKeskin, Hasan Kagan
dc.contributor.authorOyucu, Saadin
dc.contributor.authorHartman, Douglas K.
dc.contributor.authorTemur, Murat
dc.contributor.authorAydogmus, Mucahit
dc.date.accessioned2024-08-23T16:04:28Z
dc.date.available2024-08-23T16:04:28Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study examined whether an artificial intelligence-based automatic speech recognition system can accurately assess students' reading fluency and reading level. Participants were 120 fourth-grade students attending public schools in T & uuml;rkiye. Students read a grade-level text out loud while their voice was recorded. Two experts and the artificial intelligence-based automatic speech recognition system analyzed the recordings for reading errors. Following the analysis, a word error rate was calculated for both the experts and the artificial intelligence-based automatic speech recognition system. Word error rates were converted into reading accuracy rate scores. Inter-rater agreement and linear regression analyses were used to compare the raters' reading fluency scores, and logistic regression analyses were used to compare the classification of readers according to their reading levels. Results showed that the difference between the scores of the artificial intelligence-based automatic speech recognition system and the expert scores was minimal. This is because there was a very high level of agreement between the artificial intelligence-based automatic speech recognition system and the experts scores. Linear regression analyses showed that the artificial intelligence-based automatic speech recognition system significantly predicted the scores of experts. According to the logistic regression analysis results, the artificial intelligence-based automatic speech recognition system was at least 93% as successful as human raters in classifying readers as poor and good. These results give us hope that reading assessments at classroom, school, regional, national, and even international levels can be conducted more accurately and economically by using artificial intelligence-based systems in the coming years.en_US
dc.identifier.doi10.1080/10573569.2024.2345593
dc.identifier.issn1057-3569
dc.identifier.issn1521-0693
dc.identifier.scopus2-s2.0-85192104786en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1080/10573569.2024.2345593
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14209
dc.identifier.wosWOS:001210685700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherRoutledge Journals, Taylor & Francis Ltden_US
dc.relation.ispartofReading & Writing Quarterlyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBehavioren_US
dc.subjectAccuracyen_US
dc.titleCan Artificial Intelligence Identify Reading Fluency and Level? Comparison of Human and Machine Performanceen_US
dc.typeArticleen_US

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