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How Did the Generative Artificial Intelligence-Assisted Digital Multimodal Composing Process Facilitate the Production of Quality Digital Multimodal Compositions: Toward a Process-Genre Integrated Model

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Title: How Did the Generative Artificial Intelligence-Assisted Digital Multimodal Composing Process Facilitate the Production of Quality Digital Multimodal Compositions: Toward a Process-Genre Integrated Model
Language: English
Authors: Lianjiang Jiang (ORCID 0000-0002-6662-5332), Chun Lai (ORCID 0000-0002-7915-113X)
Source: TESOL Quarterly: A Journal for Teachers of English to Speakers of Other Languages and of Standard English as a Second Dialect. 2025 59(1):S52-S85.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 34
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Descriptors: Foreign Countries, Artificial Intelligence, Natural Language Processing, Technology Uses in Education, English (Second Language), Second Language Learning, Writing (Composition), Digital Literacy, Multiple Literacies, Learning Modalities, Technology Integration, Computer Software, Multimedia Materials, Early Adolescents, Student Projects, Computer Assisted Design
Geographic Terms: Hong Kong
DOI: 10.1002/tesq.3390
ISSN: 0039-8322
1545-7249
Abstract: The past decades witness an ongoing interest in reconceptualizing writing as digital multimodal composing (DMC). The emergence of language and multimodal generative artificial intelligence (GAI) tools leads to new forms of DMC processes and products. Yet prior studies have mostly examined non-GAI-assisted DMC, and few have explored how GAI-assisted DMC processes would facilitate students' authoring of quality DMC products. This study addresses the gaps by conceptualizing the notion of quality DMC product, and comparing the composing processes and products of 14 GAI-assisted DMC groups against 10 non-GAI groups across four English classrooms. Multiple sources of data were collected for a mixed method study. The findings show that while non-GAI groups demonstrated similar performance outcomes with GAI groups in base units, layouts, and rhetorical relations, the GAI groups had significantly higher scores in the development of purpose, outline/navigation skills, and their overall DMC outcomes. A taxonomy of GAI's affordances in facilitating DMC was also identified. Based on the findings, the study develops a process-genre integrated GAI-assisted DMC model for future teachers to guide students' GAI-assisted DMC processes toward quality DMC product. This integrated model links DMC processes to products and specifies the roles of teachers and students when composing with GAI.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1490722
Database: ERIC
FullText Text:
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  Data: <searchLink fieldCode="AR" term="%22Lianjiang+Jiang%22">Lianjiang Jiang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6662-5332">0000-0002-6662-5332</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chun+Lai%22">Chun Lai</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7915-113X">0000-0002-7915-113X</externalLink>)
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 10.1002/tesq.3390
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  Label: Abstract
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  Data: The past decades witness an ongoing interest in reconceptualizing writing as digital multimodal composing (DMC). The emergence of language and multimodal generative artificial intelligence (GAI) tools leads to new forms of DMC processes and products. Yet prior studies have mostly examined non-GAI-assisted DMC, and few have explored how GAI-assisted DMC processes would facilitate students' authoring of quality DMC products. This study addresses the gaps by conceptualizing the notion of quality DMC product, and comparing the composing processes and products of 14 GAI-assisted DMC groups against 10 non-GAI groups across four English classrooms. Multiple sources of data were collected for a mixed method study. The findings show that while non-GAI groups demonstrated similar performance outcomes with GAI groups in base units, layouts, and rhetorical relations, the GAI groups had significantly higher scores in the development of purpose, outline/navigation skills, and their overall DMC outcomes. A taxonomy of GAI's affordances in facilitating DMC was also identified. Based on the findings, the study develops a process-genre integrated GAI-assisted DMC model for future teachers to guide students' GAI-assisted DMC processes toward quality DMC product. This integrated model links DMC processes to products and specifies the roles of teachers and students when composing with GAI.
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      – Text: English
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        StartPage: S52
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Artificial Intelligence
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      – TitleFull: How Did the Generative Artificial Intelligence-Assisted Digital Multimodal Composing Process Facilitate the Production of Quality Digital Multimodal Compositions: Toward a Process-Genre Integrated Model
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