A key dimension of L2 writing development is learners’ ability to use words across a wider and more differentiated range of senses, yet this development remains difficult to capture at scale. This study proposes a scalable corpus-based approach to analyzing lexical semantic development at the sense level, using large language models for sense annotation and validating their performance against dictionary examples and human-annotated learner sentences. Focusing on 136 selected high-frequency polysemous lemmas, we compare sense distributions across 16 proficiency levels in EFCAMDAT with those in the written component of the BNC. Two main developmental patterns emerged. First, Jensen–Shannon distance between learner and native reference sense distributions decreased overall with proficiency, indicating increasing alignment with native reference usage. Second, entropy increased with proficiency, suggesting greater diversification of sense use. Exploratory item-level analysis showed that individual lemmas followed heterogeneous and sometimes non-linear trajectories, partly associated with differences in writing topic and genre. These findings suggest that L2 lexical development in writing involves not only changes in the frequency of word use but also the gradual expansion and differentiation of sense repertoires. The study also offers pedagogical implications for vocabulary instruction, writing development, and sense-aware assessment in second language education.
In this response, I present a critical digital literacies perspective on writing with AI. I argue that this approach and the associated notion of ‘design’, provides a powerful lens through which to view the present moment of disruption and transformation. In particular, I consider what a digital literacies approach has to say about the impact of AI on writing needs, the design of a writing process, and the design of writing tasks. I conclude with a call to interrogate questions like what kind of AI we wish to develop, what kind of relationship we wish to have with AI, and how human-centered AI movements can inform the answers to these questions.
In this response to Ken Hyland’s (2026) focus paper, Writing in the AI era, I discuss several recent studies that exemplify the type of research that Hyland argues we need more of, namely, studies of AI use across varying L2 writing proficiencies and contexts. More specifically, I focus on how these studies help to clarify and complicate our understanding of AI’s impact on writer agency and learning.
