About This Special Issue (print version)

Guest Editors: Javad Zare, Kosar University of Bojnord, Iran, and Alex Boulton, Université de Lorraine, France

One of the pivotal elements contributing to effective second language acquisition (SLA) is exposure to authentic instances of language use (O’Keeffe, 2021). Authentic language samples afford learners the opportunity to engage with the kind of language used in real-life contexts, thereby enhancing their understanding of language structure and use. Given that corpora furnish learners with authentic language data and opportunities to analyze language patterns and structures, they can be regarded as an invaluable means for exposure to authentic naturally occurring language (Pérez-Paredes, 2022). Corpora, as large structured collections of written or spoken authentic language data, offer learners a valuable resource to explore real-world language use and gain insights into their structure and use (Biber et al., 1998). They can be used directly through data-driven learning (DDL) or indirectly as a resource for developing teaching materials (Leech, 1997). DDL entails learners actively exploring authentic language data to develop their linguistic competence (Johns, 1990). It fosters a discovery-based language learning process, enabling learners to analyze and discern language patterns independently, thereby leading to a more profound SLA (Flowerdew, 2015; Zare & Aghajani, 2023).

Since its inception, DDL has garnered significant interest, as evidenced by the proliferation of studies assessing its efficacy within SLA contexts. Research findings indicate that DDL substantially enhances SLA outcomes (e.g., Crosthwaite & Steeples, 2022; Chambers, 2010; Zare & Aghajani, 2023; Zare, 2020; Zare et al., 2023). Despite its alignment with learner-centered approaches which have been the focus of attention in research and practice in SLA (Barbieri & Eckhardt, 2007; Crosthwaite, 2020; Zare & Aqajani, 2023), DDL has not been widely implemented in L2 classrooms (Boulton, 2010; O’Keeffe, 2021).

At the same time, recent advances in the field of artificial intelligence (AI) have led to the emergence of generative AI-based chatbots, among which ChatGPT stands out. Offering learners an interactive and conversational learning experience, ChatGPT presents a promising avenue for SLA (e.g., Huang et al., 2023). The adaptive nature of these chatbots allows for personalized language instruction and support, aligning with the current emphasis on learner-centered approaches in SLA (Barrot, 2023; Dörnyei, 2005; Su et al., 2023). Despite its potential, research on the use of generative AI for SLA is scarce, due to its recent introduction and concerns about its ethical implementation in educational settings (Barrot, 2023). Moreover, studying the applications of generative AI for implementing DDL is a novel concept, underscoring the necessity for further exploration in this specific domain.

The incorporation of generative AI and DDL in SLA holds substantial implications and applications. Gaining an understanding of the potential advantages, challenges, and effective strategies for utilizing generative AI-based tools in implementing DDL may transform discovery-based language learning approaches in SLA. Therefore, it is imperative to understand how generative AI-based tools can be seamlessly integrated with DDL in L2 classes. This special issue seeks to invite academics to delve into the application of generative AI-based tools, like ChatGPT, in implementing DDL in language classrooms. This exploration may pave the way for innovative discovery-based L2 learning approaches, fostering tailored L2 instruction.

The expected impact of this special issue includes uncovering effective strategies for implementing generative AI-based tools in DDL, exploring learner perspectives on using such tools, and investigating their impact on L2 learning outcomes. The findings will provide valuable insights into the potential benefits, challenges, and strategies for integrating generative AI and DDL in L2 learning environments.

Guidelines for Authors

For author guidelines for the full manuscripts, please refer to LLT submission guidelines.

Abstracts for this special issue Call for Papers should be no more than 500 words and should describe the study’s aim, methodology, findings related to L2 learning outcomes, and how these findings can be used in classroom contexts to enhance L2 teaching and learning with technology. To be considered for this special issue, which will appear in volume 31, issue 2 in February of 2027, please submit a title and a 500-word abstract through this online form by June 1, 2025. 

Publication Schedule 

February 1, 2025: Call for papers 

June 1, 2025: Submission deadline for abstracts 

July 1, 2025: Invitation for authors to submit full manuscripts

November 1, 2025: Submission of the first drafts of full manuscripts 

July 1, 2026: Submission of revised manuscripts 

February 1, 2027: Publication of final manuscripts in the special issue

For Further Information 

Please contact the Managing Editor at llt@hawaii.edu with questions. 

References

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Su, Y., Lin, Y., & Lai, C. (2023). Collaborating with ChatGPT in argumentative writing classrooms. Assessing Writing, 57, 100752. https://doi.org/10.1016/j.asw.2023.100752

Zare, J. (2020). Awareness of discourse organizers and comprehension of English academic lectures. Current Psychology, 39, 419–427. https://doi.org/10.1007/s12144-019-00579-1

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