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            <front>

                <journal-meta>
                                    <journal-id></journal-id>
            <journal-title-group>
                                                                                    <journal-title>Journal of Uludag University Faculty of Education</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2667-6788</issn>
                                                                                            <publisher>
                    <publisher-name>Bursa Uludağ University</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.19171/uefad.1728485</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>English As A Second Language</subject>
                                                            <subject>Applied Linguistics and Educational Linguistics</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>İkinci Bir Dil Olarak İngilizce</subject>
                                                            <subject>Uygulamalı Dilbilim ve Eğitim Dilbilimi </subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <article-title>Comparing Human and AI Judgments of Turkish EFL Learners’ Intelligibility</article-title>
                                                                                                                                                                                                <trans-title-group xml:lang="tr">
                                    <trans-title>İngilizceyi Yabancı Dil Olarak Öğrenen Türk Öğrencilerin Konuşma Anlaşılırlığının İnsan ve Yapay Zekâ Değerlendirmeleriyle Karşılaştırılması</trans-title>
                                </trans-title-group>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0001-5894-3974</contrib-id>
                                                                <name>
                                    <surname>Deveci</surname>
                                    <given-names>Canan</given-names>
                                </name>
                                                                    <aff>University of Utah</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20260430">
                    <day>04</day>
                    <month>30</month>
                    <year>2026</year>
                </pub-date>
                                        <volume>39</volume>
                                        <issue>1</issue>
                                        <fpage>33</fpage>
                                        <lpage>49</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20250630">
                        <day>06</day>
                        <month>30</month>
                        <year>2025</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20250926">
                        <day>09</day>
                        <month>26</month>
                        <year>2025</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 1986, Journal of Uludag University Faculty of Education</copyright-statement>
                    <copyright-year>1986</copyright-year>
                    <copyright-holder>Journal of Uludag University Faculty of Education</copyright-holder>
                </permissions>
            
                                                                                                <abstract><p>This study investigates how the intelligibility of Turkish learners of English as a foreign language (EFL) is assessed by both human raters and artificial intelligence (AI), specifically ChatGPT-4, across two different speaking tasks: a controlled read-aloud passage and a spontaneous picture-description task. Drawing on intelligibility-focused pronunciation research, the study aims to explore how task type, rater type, and pronunciation features (segmental and suprasegmental) affect intelligibility ratings. 30 intermediate-level Turkish learners of English completed both tasks, and their recordings were evaluated by three native English-speaking human raters and an AI model. Quantitative results showed that spontaneous speech received higher intelligibility scores than the read-aloud task, despite including more segmental errors. Suprasegmental features such as rhythm, stress, and phrasing played a greater role in determining intelligibility across tasks. While AI ratings closely matched human judgments in most cases, discrepancies emerged, particularly in samples where prosodic nuances were critical. Qualitative analysis further revealed that both rater types frequently flagged vowel distortions, stress misplacement, and a lack of rhythmic cohesion as common intelligibility detractors. The findings underscore the importance of integrating suprasegmental instruction in EFL pronunciation pedagogy and highlight the potential role of AI tools in supporting intelligibility assessment. Nevertheless, when it comes to assessing natural, prosody-rich speech, human judgment is still crucial. By providing empirical evidence from a Turkish EFL environment and linking L2 pronunciation research with emerging technology, this study contributes to applied linguistics.</p></abstract>
                                                                                                                                    <trans-abstract xml:lang="tr">
                            <p>Bu çalışma, İngilizceyi yabancı dil olarak öğrenen Türk öğrencilerin İngilizce konuşma anlaşılırlığının insan değerlendiriciler ve yapay zeka (ChatGPT-4) tarafından biri kontrollü okuma ve diğeri serbest resim betimleme olmak üzere iki farklı konuşma görevi üzerinden nasıl değerlendirildiğini incelemektedir. Anlaşılırlık odaklı sesletim araştırmalarından hareketle, çalışmada görev türü, değerlendirici türü ve sesletim özelliklerinin (parçalı ve parçalarüstü ses birim) anlaşılırlık puanlarını nasıl etkilediği araştırılmıştır. Orta düzeyde İngilizce yeterliliğe sahip 30 Türk öğrenci her iki görevi tamamlamış ve ses kayıtları anadili İngilizce olan üç değerlendirici ve bir yapay zeka sürümü olan ChatGPT-4 tarafından değerlendirilmiştir. Nicel bulgular, serbest resim betimleme görevinin parçalı sesbirim hatalarını daha fazla barındırmasına rağmen okuma görevine kıyasla daha yüksek anlaşılırlık puanları aldığını göstermiştir. Özellikle ritim, vurgu ve söz gruplaması gibi parçalarüstü ses birim özelliklerin anlaşılırlığın belirlenmesinde daha etkili olduğu görülmüştür. Yapay zeka puanlamaları çoğu durumda insan değerlendirmeleriyle yakınlık göstermiş, ancak özellikle prosodi ayrıntıların kritik olduğu örneklerde farklılıklar ortaya çıkmıştır. Nitel analiz, her iki değerlendirici türünün de sıkça ünlü ses bozulmaları, yanlış vurgu ve ritmik bütünlük eksikliğini anlaşılırlığı düşüren unsurlar olarak işaretlediğini ortaya koymuştur. Bulgular, İngilizce öğretiminde parçalarüstü ses birim özelliklerin daha güçlü biçimde ele alınması gerektiğini vurgulamakta ve anlaşılırlık değerlendirmesinde yapay zeka araçlarının potansiyel rolüne dikkat çekmektedir. Bununla birlikte, özellikle prosodi açısından daha zengin olan serbest konuşma değerlendirilmesinde insan yargısı vazgeçilmezdir. Bu çalışma, ikinci dil sesletim araştırmalarını gelişen teknolojilerle buluşturarak uygulamalı dilbilime katkı sağlamakta ve yabancı dil olarak İngilizce öğretimi bağlamından deneysel kanıtlar sunmaktadır.</p></trans-abstract>
                                                            
            
                                                            <kwd-group>
                                                    <kwd>Intelligibility</kwd>
                                                    <kwd>  Pronunciation</kwd>
                                                    <kwd>  Turkish EFL learners</kwd>
                                                    <kwd>  AI</kwd>
                                                    <kwd>  ChatGPT</kwd>
                                            </kwd-group>
                                                        
                                                                            <kwd-group xml:lang="tr">
                                                    <kwd>Anlaşılırlık</kwd>
                                                    <kwd>  Sesletim</kwd>
                                                    <kwd>  İngilizceyi yabancı dil olarak öğrenen Türk öğrenciler</kwd>
                                                    <kwd>  Yapay zeka</kwd>
                                                    <kwd>  ChatGPT</kwd>
                                            </kwd-group>
                                                                                                            </article-meta>
    </front>
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