generative-ai
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- PaperSystemForthcoming · 1 Aug 2026
“I Haven't felt AI writing is taking away my agency as a person”: A longitudinal study of ESL students' use of GenAI in academic literacies development
Xiao Tan
A longitudinal study of ESL students found that they do not perceive generative AI writing tools as diminishing their personal agency in developing academic literacies.
- PaperTESOL Quarterly22 Jul 2026
Unveiling the AI Divide: A Comparative Study of Novice and Veteran EFL Teachers' Experience of Using Generative AI in Teaching
Qi Lin
A comparative study of novice and veteran EFL teachers reveals that the AI divide emerges mainly at the levels of AI capability and AI outcomes rather than access, shaped by factors such as perceived ease of use, educational background, social support, and professional development opportunities.
Original abstract
The rapid advancement of artificial intelligence (AI) has brought both opportunities and challenges to the field of education. Within this evolving landscape, the emergence of the AI divide has become a pressing concern, particularly in teacher education and EFL instruction. This study developed a three‐level three‐factor conceptual framework to explore the AI divide through the dimensions of AI access, AI capability, and AI outcomes, while also considering technological, personal, and institutional influences. Using this framework, the study employed a comparative research design to examine how novice and veteran EFL teachers integrated AI differently in their teaching practices. The findings revealed that the AI divide between novice and veteran EFL teachers mostly occurred at the second level (AI capability) and third level (AI outcomes), rather than the first level (AI access). These disparities were shaped by a combination of technological, personal, and institutional factors, including perceived ease of use of AI, educational background, social support, and access to AI‐related professional development. This study extends the concept of AI divide into the field of EFL education and offers valuable theoretical insights and practical implications for more equitable AI integration in EFL teaching.
- PaperComputer Assisted Language Learning22 Jun 2026
Exploring L2 learners’ self-regulation and engagement with Corpus-GenAI data-driven learning in academic writing revision
Yanhong Liu, Hongliang Yu, Shanhua He, Li (Francoise) Yang
This study investigates how L2 learners use self-regulation strategies and engage with a combined Corpus-GenAI data-driven learning approach during academic writing revision. It examines the effectiveness of integrating corpus tools and generative AI to support writing improvement.
- PaperReCALL18 Jun 2026
Learners’ priority criteria for evaluating data-driven learning tools: An analytic hierarchy process study with traditional and GenAI-based concordancers
Hansol Lee, Jang Ho Lee
This study uses analytic hierarchy process to examine Korean EFL learners' priority criteria for evaluating data-driven learning tools, including both traditional and GenAI-based concordancers. Six criteria were identified, and learners showed differentiated importance weights that related to their tool preference. The findings demonstrate AHP's utility for perception-based evaluations of DDL tools.
Original abstract
This study investigates how language learners prioritize criteria for evaluating data-driven learning (DDL) tools and how these priorities relate to tool preference. While previous research has proposed multiple criteria for evaluating the perceived effectiveness of DDL tools in capturing the emic dimension of the learner experience, empirical work has scantily examined how learners differentially prioritize these criteria, or how such priority structures can be empirically incorporated into systematic comparisons of tools. Given the recent emergence of generative artificial intelligence (GenAI) in language learning, this study examines how GenAI-based tools may contribute to DDL when judged against learner-valued effectiveness criteria. Drawing on a literature review, six criteria were identified: sentence comprehensibility, relevance to semantic learning, relevance to syntactic learning, perceived pedagogical value, accessibility for independent use, and support for autonomous learning. Thirty-five Korean EFL university students completed an analytic hierarchy process (AHP) task. First, they made pairwise comparisons among the six criteria, yielding priority weights that indicated the relative importance learners attach to each aspect of DDL effectiveness. Second, the study compared the perceived effectiveness of a traditional online concordancer that accesses the British National Corpus with that of a custom-developed GenAI-based concordancer, prioritizing these weighted criteria. The results indicate that learners assign differentiated importance to the six criteria and that these priority patterns are associated with the concordance they regard as more effective. This study demonstrates the usefulness of AHP for modelling multi-criteria, perception-based evaluations of DDL tools and incorporating learners’ priority judgements into the comparison of alternative tool designs.
- PaperComputer Assisted Language Learning11 Jun 2026
The impact of GenAI chatbots on communicative competence in EFL writing in Chinese upper secondary school
Zhoutiao Li
This study examines how GenAI chatbots affect communicative competence in EFL writing among Chinese upper secondary school students.
- PaperComputer Assisted Language Learning11 Jun 2026
From declarative knowledge to procedural fluency: a generative AI tutor for Arabic agreement rules
Djemai Mahmoud Boulaares
This paper proposes a generative AI tutor designed to help Arabic learners transition from declarative knowledge of agreement rules to procedural fluency in applying them.
- PaperComputer Assisted Language Learning7 May 2026
Comparative analysis of high and low performers’ use of generative AI in EFL academic writing: behavioral patterns and perceptions
Yao Lu, Chengyuan Jia
This study compares how high and low performing EFL students use generative AI tools in academic writing, examining their behavioral patterns and perceptions.
- PaperComputer Assisted Language Learning5 May 2026
Learning analytics on multimodal GAI-driven EFL oral learning: uncovering learning behavior clusters with motivation and performance dynamics
Yuting Chen, Morris Siu-Yung Jong, Michael Yi-Chao Jiang, Ming Li
This study uses learning analytics to analyze multimodal data from generative AI-driven EFL oral learning, identifying clusters of learning behaviors and examining their relationships with motivation and performance dynamics.