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Content Adaptation for Language Learning: A Hybrid AI Approach

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Title: Content Adaptation for Language Learning: A Hybrid AI Approach
Language: English
Authors: Jatin Arora, Irina Elgort (ORCID 0000-0002-4568-9951), Junhong Zhao (ORCID 0000-0001-7031-3828)
Source: The EUROCALL Review. 2025 32(2):169-182.
Availability: European Association for Computer-Assisted Language Learning (EUROCALL). EUROCALL Headquarters, School of Modern Languages, University of Ulster, Cromore Road, Coleraine BT52 1SA, Northern Ireland, UK. Tel: +34-67-943-1283; Web site: http://www.eurocall-languages.org/
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Second Language Learning, Artificial Intelligence, Intelligent Tutoring Systems, Multimedia Materials, Word Lists, Word Frequency, Vocabulary Development, Pilot Projects, Novices, Language Proficiency, Difficulty Level, Independent Study, Reading Comprehension, Listening Comprehension
ISSN: 1695-2618
Abstract: In learning a foreign language, access to comprehensible input is a critical success factor. However, at early stages, when learners are still below an intermediate-proficiency level, finding level-appropriate and engaging materials is highly problematic. Although the Internet abounds in text and multimedia materials in many languages, most of them are too difficult to be useful for lower-proficiency language learners. The present project aimed to establish whether the affordances of large language models (LLMS) can be harnessed to turn authentic audio, video, and text materials into comprehensible input for independent elementary-level language learners. The present article reports on the outcomes of a research and development project that adopts a hybrid approach to simplifying authentic materials, combining affordances of LLMs with careful prompt engineering and rule-based refinement. The article details the hybrid sequential pipeline system and the results of two rounds of evaluation: language teacher ratings and automated text analysis indices. Based on the outcome of these evaluations, it is concluded that the proposed approach can provide an efficient way of simplifying authentic content for and by lower-proficiency language learners. Directions for future research and development are also proposed.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1494400
Database: ERIC
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  Data: Content Adaptation for Language Learning: A Hybrid AI Approach
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  Data: <searchLink fieldCode="AR" term="%22Jatin+Arora%22">Jatin Arora</searchLink><br /><searchLink fieldCode="AR" term="%22Irina+Elgort%22">Irina Elgort</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4568-9951">0000-0002-4568-9951</externalLink>)<br /><searchLink fieldCode="AR" term="%22Junhong+Zhao%22">Junhong Zhao</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7031-3828">0000-0001-7031-3828</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22The+EUROCALL+Review%22"><i>The EUROCALL Review</i></searchLink>. 2025 32(2):169-182.
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  Data: European Association for Computer-Assisted Language Learning (EUROCALL). EUROCALL Headquarters, School of Modern Languages, University of Ulster, Cromore Road, Coleraine BT52 1SA, Northern Ireland, UK. Tel: +34-67-943-1283; Web site: http://www.eurocall-languages.org/
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  Data: Y
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  Data: 14
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  Data: 2025
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+Tutoring+Systems%22">Intelligent Tutoring Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Multimedia+Materials%22">Multimedia Materials</searchLink><br /><searchLink fieldCode="DE" term="%22Word+Lists%22">Word Lists</searchLink><br /><searchLink fieldCode="DE" term="%22Word+Frequency%22">Word Frequency</searchLink><br /><searchLink fieldCode="DE" term="%22Vocabulary+Development%22">Vocabulary Development</searchLink><br /><searchLink fieldCode="DE" term="%22Pilot+Projects%22">Pilot Projects</searchLink><br /><searchLink fieldCode="DE" term="%22Novices%22">Novices</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Proficiency%22">Language Proficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+Study%22">Independent Study</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Comprehension%22">Reading Comprehension</searchLink><br /><searchLink fieldCode="DE" term="%22Listening+Comprehension%22">Listening Comprehension</searchLink>
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  Data: 1695-2618
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  Data: In learning a foreign language, access to comprehensible input is a critical success factor. However, at early stages, when learners are still below an intermediate-proficiency level, finding level-appropriate and engaging materials is highly problematic. Although the Internet abounds in text and multimedia materials in many languages, most of them are too difficult to be useful for lower-proficiency language learners. The present project aimed to establish whether the affordances of large language models (LLMS) can be harnessed to turn authentic audio, video, and text materials into comprehensible input for independent elementary-level language learners. The present article reports on the outcomes of a research and development project that adopts a hybrid approach to simplifying authentic materials, combining affordances of LLMs with careful prompt engineering and rule-based refinement. The article details the hybrid sequential pipeline system and the results of two rounds of evaluation: language teacher ratings and automated text analysis indices. Based on the outcome of these evaluations, it is concluded that the proposed approach can provide an efficient way of simplifying authentic content for and by lower-proficiency language learners. Directions for future research and development are also proposed.
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  Data: 2026
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  Data: EJ1494400
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 169
    Subjects:
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Multimedia Materials
        Type: general
      – SubjectFull: Word Lists
        Type: general
      – SubjectFull: Word Frequency
        Type: general
      – SubjectFull: Vocabulary Development
        Type: general
      – SubjectFull: Pilot Projects
        Type: general
      – SubjectFull: Novices
        Type: general
      – SubjectFull: Language Proficiency
        Type: general
      – SubjectFull: Difficulty Level
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      – SubjectFull: Independent Study
        Type: general
      – SubjectFull: Reading Comprehension
        Type: general
      – SubjectFull: Listening Comprehension
        Type: general
    Titles:
      – TitleFull: Content Adaptation for Language Learning: A Hybrid AI Approach
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            NameFull: Jatin Arora
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            NameFull: Irina Elgort
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            NameFull: Junhong Zhao
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              Y: 2025
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