Artificial intelligence in aviation English testing
In the field of aviation, English language proficiency is essential for ensuring clear communication and safe flight operations. Effective assessment of pilots’ and air traffic controllers’ aviation English (AE) proficiency is, therefore, crucial. Conventional AE proficiency assessments, while effec...
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| Format: | Article |
| Language: | English |
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Literacy Trek
2024-12-01
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| Series: | Literacy Trek |
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| Online Access: | https://dergipark.org.tr/en/pub/literacytrek/issue/89658/1556603 |
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| author | Gökhan Demirdöken |
| author_facet | Gökhan Demirdöken |
| author_sort | Gökhan Demirdöken |
| collection | DOAJ |
| description | In the field of aviation, English language proficiency is essential for ensuring clear communication and safe flight operations. Effective assessment of pilots’ and air traffic controllers’ aviation English (AE) proficiency is, therefore, crucial. Conventional AE proficiency assessments, while effective, face limitations in scalability, objectivity, and feedback mechanisms. This article reviews the advancements and effectiveness of AI-driven assessment tools for AE proficiency testing, highlighting their potential to overcome these limitations. The review encompasses AI technologies such as automated speech recognition (ASR), natural language processing (NLP), and intelligent tutoring systems (ITS) in the light of the language proficiency requirements stated by the International Civil Aviation Organization (ICAO). Overall, the present review concludes that AI-driven tools provide accurate, reliable, and immediate feedback, significantly improving learners' AE proficiency. Despite challenges such as speech recognition errors and ethical concerns, these tools offer scalable and accessible solutions for large aviation training programs. The review concludes with recommendations for future research, emphasizing the need for continued innovation to address technological limitations and enhance adaptive learning environments. This review offers valuable insights for English for Specific Purposes (ESP) practitioners and stakeholders in the aviation industry. |
| format | Article |
| id | doaj-art-f372f90c5ab149f49d960a3d094017f2 |
| institution | Kabale University |
| issn | 2602-3768 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Literacy Trek |
| record_format | Article |
| series | Literacy Trek |
| spelling | doaj-art-f372f90c5ab149f49d960a3d094017f22025-01-21T15:05:13ZengLiteracy TrekLiteracy Trek2602-37682024-12-01103362384https://doi.org/10.47216/literacytrek.1556603Artificial intelligence in aviation English testingGökhan Demirdöken0https://orcid.org/0000-0002-9442-0625Independent Researcher, GermanyIn the field of aviation, English language proficiency is essential for ensuring clear communication and safe flight operations. Effective assessment of pilots’ and air traffic controllers’ aviation English (AE) proficiency is, therefore, crucial. Conventional AE proficiency assessments, while effective, face limitations in scalability, objectivity, and feedback mechanisms. This article reviews the advancements and effectiveness of AI-driven assessment tools for AE proficiency testing, highlighting their potential to overcome these limitations. The review encompasses AI technologies such as automated speech recognition (ASR), natural language processing (NLP), and intelligent tutoring systems (ITS) in the light of the language proficiency requirements stated by the International Civil Aviation Organization (ICAO). Overall, the present review concludes that AI-driven tools provide accurate, reliable, and immediate feedback, significantly improving learners' AE proficiency. Despite challenges such as speech recognition errors and ethical concerns, these tools offer scalable and accessible solutions for large aviation training programs. The review concludes with recommendations for future research, emphasizing the need for continued innovation to address technological limitations and enhance adaptive learning environments. This review offers valuable insights for English for Specific Purposes (ESP) practitioners and stakeholders in the aviation industry.https://dergipark.org.tr/en/pub/literacytrek/issue/89658/1556603ai-driven assessmentartificial intelligenceaviation englishgenerative ailanguage proficiency testing |
| spellingShingle | Gökhan Demirdöken Artificial intelligence in aviation English testing Literacy Trek ai-driven assessment artificial intelligence aviation english generative ai language proficiency testing |
| title | Artificial intelligence in aviation English testing |
| title_full | Artificial intelligence in aviation English testing |
| title_fullStr | Artificial intelligence in aviation English testing |
| title_full_unstemmed | Artificial intelligence in aviation English testing |
| title_short | Artificial intelligence in aviation English testing |
| title_sort | artificial intelligence in aviation english testing |
| topic | ai-driven assessment artificial intelligence aviation english generative ai language proficiency testing |
| url | https://dergipark.org.tr/en/pub/literacytrek/issue/89658/1556603 |
| work_keys_str_mv | AT gokhandemirdoken artificialintelligenceinaviationenglishtesting |