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The public release and surprising capacity of ChatGPT has brought AI-enabled text generation into the forefront for educators and academics. ChatGPT and similar text generation tools raise numerous questions for educational practitioners, policymakers, and researchers. We begin by first describing what large language models are and how they function, and then situate them in the history of technology’s complex interrelationship with literacy, cognition, and education. Finally, we discuss implications for the field.

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We investigated how learner factors, such as vocabulary proficiency, strategy use, and working memory, are associated with successful corpus‐based second language (L2) vocabulary learning, in which learners are encouraged to analyze and explore large, structured collections of authentic language data (i.e., corpora) to resolve their lexical issues (i.e., data‐driven learning [DDL]). After measuring L2 vocabulary proficiency and working memory capacity, 35 South Korean college students performed a DDL activity during an English reading task using a think‐aloud protocol to document their strategy use. Through this we identified participants’ lexical inferencing strategy use, including DDL‐focused strategies, based on qualitative coding. Using path analysis, we identified that participants’ DDL‐focused strategy use largely influenced their vocabulary acquisition and retention, highlighting the pedagogical advantages of these strategies for successful DDL. We found that participants’ L2 vocabulary proficiency and working memory contributed to their vocabulary acquisition and retention, indicating the roles of these factors in managing cognitive load in DDL. Future investigation into the causal relationship between improved working memory and corpus‐based L2 vocabulary learning and the role of other learner factors, including motivation and learning style, is needed to extend our understanding of DDL.

In this study, we used a data-mining approach to identify hidden groups in a corpus-based second-language (L2) vocabulary experiment. After a vocabulary pre-test, a total of 132 participants performed three online reading tasks (in random orders) equipped with the following glossary types: (1) concordance lines and definitions of target lexical items, (2) concordance lines of target lexical items, and (3) no glossary information. Although the results of a previous study based on variable-centred analysis (i.e. multiple regression analysis) revealed that more glossary information could lead to better learning outcomes (Lee, Warschauer & Lee, 2017), using a model-based clustering technique in the present study allowed us to unearth learner types not identified in the previous analysis. Instead of the performance pattern found in the previous study (more glossary led to higher gains), we identified one learner group who exhibited their ability to make successful use of concordance lines (and thus are optimized for data-driven learning, or DDL; Johns, 1991), and another group who showed limited L2 vocabulary learning when exposed to concordance lines only. Further, our results revealed that L2 proficiency intersects with vocabulary gains of different learner types in complex ways. Therefore, using this technique in computer-assisted language learning (CALL) research to understand differential effects of accommodations can help us better identify hidden learner types and provide personalized CALL instruction.