Taif Abdulhussein Dakhil

The present study aimed to find the effect of artificial intelligence (AI)-mediated speaking assessment on speaking performance and willingness to communicate (WTC) of Iraqi EFL Learners. More specifically, the study sought to find whether AI-mediated speaking assessment enhance the speaking performance (grammar, vocabulary, pronunciation, intonation, and fluency) of Intermediate Iraqi EFL learners and whether AI-mediated speaking assessment enhanced their WTC in English. In so doing, 40 intermediate Iraqi EFL learners were randomly selected and assigned into experimental and control groups, each comprising 20 learners. The experimental group participants received ten 60-minute treatment sessions with ELSA Speech Analyzer, while the control group received no treatment. The speaking pre-test of both groups was run prior to the treatment, and all participants were post-tested at the end of the study. The Willingness to Communicate in a Foreign-Language Scale was also administered to groups prior to and after the treatment. A speaking assessment rubric, including vocabulary, grammar, intonation, pronunciation, and fluency, was used to assess the speaking performance of both groups. The findings demonstrated that AI-mediated speaking assessment enhanced the grammar, vocabulary, intonation, and fluency of the experimental group. However, the two groups did not differ in terms of pronunciation. Furthermore, this assessment tool enhanced the willingness to communicate with native speakers, the willingness to communicate with non-native speakers, and the willingness to communicate in the school context of this group. In general, the speaking assessment mediated by AI significantly enhanced the speaking performance and WTC of the learners. These findings might advance the current scholarly discourse on AI within the domains of language pedagogy and assessment.