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A Theory-Informed Framework for Selecting AI Tools in Language Teaching

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Title: A Theory-Informed Framework for Selecting AI Tools in Language Teaching
Language: English
Authors: Xuanxuan Zhou (ORCID 0009-0006-7070-8348), Nur Ainil Sulaiman (ORCID 0000-0001-6212-7494), Hanita Hanim Ismail (ORCID 0000-0003-3121-8822)
Source: International Journal of Technology in Education. 2026 9(1):208-222.
Availability: International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about
Peer Reviewed: Y
Page Count: 15
Publication Date: 2026
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Decision Making, Second Language Instruction, Communicative Competence (Languages), Pedagogical Content Knowledge, Technological Literacy, Usability, Value Judgment, Computer Assisted Instruction
ISSN: 2689-2758
Abstract: The revolutionary progress of Artificial Intelligence (AI) is redefining educational technology, enabling innovative approaches to education. However, the absence of a theory-informed support for selecting AI tools has raised concerns about instructional consistency and quality in language teaching. To address this gap, this study proposes an AI Tool Selection (ATS) Framework to guide educators in choosing AI tools for effective language teaching. To ensure theoretical rigor, the proposed framework synthesizes insights from nine established theories across three interrelated components: Pedagogical Alignment, informed by CLT, CALL, and SLA; Technological Integration, drawing on SAMR, TPACK, and HCI; and Adoption and Usability, grounded in TAM, Sociocultural Theory and DOI. Each component is defined by three clear indicators and guiding questions that prompt informed, context-sensitive decisions in AI tool selection. Overall, the conceived ATS Framework advances AI tool selection in language teaching by offering operational practicality, theoretical depth, and ethical-contextual sensitivity, ensuring that decisions are actionable, conceptually grounded, and culturally responsible. Future research should empirically validate and refine the framework across diverse educational and cultural contexts.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1494098
Database: ERIC
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  Data: A Theory-Informed Framework for Selecting AI Tools in Language Teaching
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  Data: <searchLink fieldCode="AR" term="%22Xuanxuan+Zhou%22">Xuanxuan Zhou</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-7070-8348">0009-0006-7070-8348</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nur+Ainil+Sulaiman%22">Nur Ainil Sulaiman</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6212-7494">0000-0001-6212-7494</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hanita+Hanim+Ismail%22">Hanita Hanim Ismail</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3121-8822">0000-0003-3121-8822</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Technology+in+Education%22"><i>International Journal of Technology in Education</i></searchLink>. 2026 9(1):208-222.
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  Data: International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about
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  Data: The revolutionary progress of Artificial Intelligence (AI) is redefining educational technology, enabling innovative approaches to education. However, the absence of a theory-informed support for selecting AI tools has raised concerns about instructional consistency and quality in language teaching. To address this gap, this study proposes an AI Tool Selection (ATS) Framework to guide educators in choosing AI tools for effective language teaching. To ensure theoretical rigor, the proposed framework synthesizes insights from nine established theories across three interrelated components: Pedagogical Alignment, informed by CLT, CALL, and SLA; Technological Integration, drawing on SAMR, TPACK, and HCI; and Adoption and Usability, grounded in TAM, Sociocultural Theory and DOI. Each component is defined by three clear indicators and guiding questions that prompt informed, context-sensitive decisions in AI tool selection. Overall, the conceived ATS Framework advances AI tool selection in language teaching by offering operational practicality, theoretical depth, and ethical-contextual sensitivity, ensuring that decisions are actionable, conceptually grounded, and culturally responsible. Future research should empirically validate and refine the framework across diverse educational and cultural contexts.
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      – Text: English
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        StartPage: 208
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Educational Technology
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Communicative Competence (Languages)
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      – SubjectFull: Usability
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      – SubjectFull: Value Judgment
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      – SubjectFull: Computer Assisted Instruction
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