Riyadh Abbas Ubeid AL-Jashami

Improving the way of designing syllabus by making it more tailored to the needs and interest of the learners has been an important objective in language teaching, which leads to selecting and grading instructional materials with a consistent attention. This study focused on studying some dark areas of material selection in language teaching, aiming to make instructional materials more attractive to learners. There was also an attempt to see if learner's interest could affect reading and grammar differently. Lastly, the research also sought to find learners' perception on the use of interest in material selection and its possible effect in their performance. A mixed-method design was used to answer the research questions. The findings and results revealed that the use of interest in selecting class materials helped learners have better performance in reading, and the improvement showed a high correlation with learners' performance in grammar learning. Furthermore, the results of the questions asking for learners' perceptions disclosed the effectiveness of integrating interest to teaching materials in language learning. Researchers and teachers are suggested to seek ways to assist the syllabus planners to grow achievement by changing the materials to be more interesting, which can facilitate the language teaching and learning.

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.