The Use of Artificial Intelligence to Promote Autonomous Pronunciation Learning: Segmental and Suprasegmental Features Perspective
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| Title: | The Use of Artificial Intelligence to Promote Autonomous Pronunciation Learning: Segmental and Suprasegmental Features Perspective |
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| Authors: | Senowarsito, Sukma Nur Ardini |
| Source: | Indonesian Journal of English Language Teaching and Applied Linguistics. 2023 8(2):133-147. |
| Availability: | Indonesian Journal of English Language Teaching and Applied Linguistics. English Department, Faculty of Education and Teacher Training, State Islamic Institute of Samarinda, Indonesia. e-mail: ijeltalj@gmail.com; Web site: https://ijeltal.org/index.php/ijeltal |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Suprasegmentals, Pronunciation Instruction, Language Proficiency, Intonation, Phonology, Language Rhythm, Web Sites, Phonemes, Correlation, Teaching Methods, Computer Software, Second Language Learning, Second Language Instruction, Undergraduate Students, English (Second Language), Independent Study, Foreign Countries |
| Geographic Terms: | Indonesia |
| ISSN: | 2527-6492 2527-8746 |
| Abstract: | The study aimed at investigating the effects of autonomous pronunciation learning using AI as well as the experiences of autonomous pronunciation learning using AI by higher level students. Explanatory sequential mixed-method research using both quantitative and qualitative methods was employed within thirty-two students from Universitas PGRI Semarang's first-year students serving as the sample. Assessments, interviews, and an evaluation of instructional materials were used as the instruments. Through pre- and post-testing, quantitative analysis was used to evaluate the students' pronunciation proficiency. Quantitative data analysis was done using SPSS. However, a qualitative analysis was used to review the interview. To bolster the findings of the tests, it was descriptively examined. After the treatments using an AI based application named ELSA, there was a significant correlation between the use of AI and autonomous pronunciation learning. However, ELSA has certain shortcomings. It appears to be primarily concerned with segmental than suprasegmental features. Only intonation is available from among all the features offered to practice suprasegmental features. While students found it difficult to emphasize words, there is no other practice for suprasegmental qualities. In reality, the ELSA website states that its curriculum covers core English skills such as word stress, intonation, rhythm, listening, and conversation. As a result, the ELSA creator may take this criticism into consideration as they continue to improve their product. It implies that the creator is responsive to the concerns or suggestions of their customers or users, which can contribute to the ongoing development and success of the product. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1409001 |
| Database: | ERIC |
| Abstract: | The study aimed at investigating the effects of autonomous pronunciation learning using AI as well as the experiences of autonomous pronunciation learning using AI by higher level students. Explanatory sequential mixed-method research using both quantitative and qualitative methods was employed within thirty-two students from Universitas PGRI Semarang's first-year students serving as the sample. Assessments, interviews, and an evaluation of instructional materials were used as the instruments. Through pre- and post-testing, quantitative analysis was used to evaluate the students' pronunciation proficiency. Quantitative data analysis was done using SPSS. However, a qualitative analysis was used to review the interview. To bolster the findings of the tests, it was descriptively examined. After the treatments using an AI based application named ELSA, there was a significant correlation between the use of AI and autonomous pronunciation learning. However, ELSA has certain shortcomings. It appears to be primarily concerned with segmental than suprasegmental features. Only intonation is available from among all the features offered to practice suprasegmental features. While students found it difficult to emphasize words, there is no other practice for suprasegmental qualities. In reality, the ELSA website states that its curriculum covers core English skills such as word stress, intonation, rhythm, listening, and conversation. As a result, the ELSA creator may take this criticism into consideration as they continue to improve their product. It implies that the creator is responsive to the concerns or suggestions of their customers or users, which can contribute to the ongoing development and success of the product. |
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| ISSN: | 2527-6492 2527-8746 |
