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<p>Digital multimodal composing (DMC) has the potential to significantly enhance multiliteracies in English as a Foreign Language (EFL) classrooms. Recently, generative AI (GenAI) tools have gained traction in language learning, leading to notable improvements in EFL students’ writing motivation, engagement, and abilities. The integration of GenAI into DMC instruction represents a promising educational strategy; however, the role of AI literacy in relation to writing engagement, motivation, and skills is still underexplored. This study implemented GenAI-supported DMC instruction, developed based on Systemic Functional Theory, within an EFL context involving 36 primary students from Hong Kong. It aimed to investigate the impact of this instructional approach on students’ GenAI literacy, writing engagement, motivation, and abilities, while also capturing their perspectives on their learning experiences. A mixed-methods approach was employed, utilizing both quantitative and qualitative data. Pre- and post-questionnaires indicated significant improvements in students’ GenAI literacy, writing motivation, engagement, and abilities. Focus group interviews further triangulated these findings and offered insights into their learning experiences. The results reveal that high-performing students who produced high-quality writing exhibited strong GenAI literacy and writing engagement, while low-performing students demonstrated lower levels in both areas. Interestingly, we found that some students exhibited high GenAI literacy but low writing engagement, while others displayed low GenAI literacy alongside high writing engagement. This study highlights the four-dimensional relationship between GenAI literacy and writing engagement. It contributes to the development of a comprehensive DMC pedagogy and offers practical recommendations for designing GenAI-supported DMC instruction in EFL contexts. Future research should explore diverse methodologies and gather insights from teachers, parents, and students across various educational levels.</p>

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GenAI-supported digital multimodal composing: facilitating EFL students’ GenAI literacy, writing engagement, motivation, and abilities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

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  • Xiaoxuan Fang,
  • Davy Tsz Kit Ng,
  • Doris Xunnuo Li,
  • Yi Yan Tao,
  • Hong Kit Wu,
  • Samuel Kai Wah CHU

摘要

Digital multimodal composing (DMC) has the potential to significantly enhance multiliteracies in English as a Foreign Language (EFL) classrooms. Recently, generative AI (GenAI) tools have gained traction in language learning, leading to notable improvements in EFL students’ writing motivation, engagement, and abilities. The integration of GenAI into DMC instruction represents a promising educational strategy; however, the role of AI literacy in relation to writing engagement, motivation, and skills is still underexplored. This study implemented GenAI-supported DMC instruction, developed based on Systemic Functional Theory, within an EFL context involving 36 primary students from Hong Kong. It aimed to investigate the impact of this instructional approach on students’ GenAI literacy, writing engagement, motivation, and abilities, while also capturing their perspectives on their learning experiences. A mixed-methods approach was employed, utilizing both quantitative and qualitative data. Pre- and post-questionnaires indicated significant improvements in students’ GenAI literacy, writing motivation, engagement, and abilities. Focus group interviews further triangulated these findings and offered insights into their learning experiences. The results reveal that high-performing students who produced high-quality writing exhibited strong GenAI literacy and writing engagement, while low-performing students demonstrated lower levels in both areas. Interestingly, we found that some students exhibited high GenAI literacy but low writing engagement, while others displayed low GenAI literacy alongside high writing engagement. This study highlights the four-dimensional relationship between GenAI literacy and writing engagement. It contributes to the development of a comprehensive DMC pedagogy and offers practical recommendations for designing GenAI-supported DMC instruction in EFL contexts. Future research should explore diverse methodologies and gather insights from teachers, parents, and students across various educational levels.