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Augmented Linguistic Analysis Skills: Machine Translation and Generative AI as Pedagogical Aids for Analyzing Complex English Compounds.

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Title: Augmented Linguistic Analysis Skills: Machine Translation and Generative AI as Pedagogical Aids for Analyzing Complex English Compounds.
Authors: LOOCK, RUDY1 rudy.loock@univ-lille.fr, HOLT, BENJAMIN1 benjamin.holt@univ-lille.fr
Source: Technology in Language Teaching & Learning. 2024, Vol. 6 Issue 3, p1-27. 27p.
Subject Terms: *Generative artificial intelligence, *English as a foreign language, *Limited English-proficient students, Machine translating, Linguistic analysis
Abstract: At a time when language trainers need to determine how best to integrate new digital tools that provide students with linguistic information, our aim in this article is to assess the potential value of information provided by two types of tools now widely used by students: machine translation via online translators and state-of-the-art generative AI platforms such as ChatGPT. In particular, the aim is to explore whether access to such information can help students develop their linguistic analysis skills to improve their understanding of the language prior to performing a translation task. Our case study deals with the analysis of complex English compounds--a known challenge for French learners of English as a second language--and involves two distinct cohorts: students in their third and final year of an undergraduate program in applied foreign languages, and students in their second and final year of a master's program in professional translation. We evaluated the participants' ability to identify head nouns--a necessary skill for comprehending complex noun phrases--under two conditions: (i) without access to any additional information and (ii) with access to machine translation outputs provided by a generic online translator. Subsequently, we explored the capabilities of advanced generative AI tools--in this instance ChatGPT--in correctly identifying head nouns. Our results show that students may benefit from the presence of machine translation outputs, albeit with varying degrees of success. However, our experimentation with ChatGPT suggests that generative AI tools may be more effect in helping students analyze and understand complex noun phrases in English, in particular since the launch of ChatGPT-4o. [ABSTRACT FROM AUTHOR]
Copyright of Technology in Language Teaching & Learning is the property of Castledown Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Augmented Linguistic Analysis Skills: Machine Translation and Generative AI as Pedagogical Aids for Analyzing Complex English Compounds.
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  Data: <searchLink fieldCode="JN" term="%22Technology+in+Language+Teaching+%26+Learning%22">Technology in Language Teaching & Learning</searchLink>. 2024, Vol. 6 Issue 3, p1-27. 27p.
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  Data: *<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22English+as+a+foreign+language%22">English as a foreign language</searchLink><br />*<searchLink fieldCode="DE" term="%22Limited+English-proficient+students%22">Limited English-proficient students</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+translating%22">Machine translating</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistic+analysis%22">Linguistic analysis</searchLink>
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  Data: At a time when language trainers need to determine how best to integrate new digital tools that provide students with linguistic information, our aim in this article is to assess the potential value of information provided by two types of tools now widely used by students: machine translation via online translators and state-of-the-art generative AI platforms such as ChatGPT. In particular, the aim is to explore whether access to such information can help students develop their linguistic analysis skills to improve their understanding of the language prior to performing a translation task. Our case study deals with the analysis of complex English compounds--a known challenge for French learners of English as a second language--and involves two distinct cohorts: students in their third and final year of an undergraduate program in applied foreign languages, and students in their second and final year of a master's program in professional translation. We evaluated the participants' ability to identify head nouns--a necessary skill for comprehending complex noun phrases--under two conditions: (i) without access to any additional information and (ii) with access to machine translation outputs provided by a generic online translator. Subsequently, we explored the capabilities of advanced generative AI tools--in this instance ChatGPT--in correctly identifying head nouns. Our results show that students may benefit from the presence of machine translation outputs, albeit with varying degrees of success. However, our experimentation with ChatGPT suggests that generative AI tools may be more effect in helping students analyze and understand complex noun phrases in English, in particular since the launch of ChatGPT-4o. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Technology in Language Teaching & Learning is the property of Castledown Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2024
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