AI-assisted task-based language teaching: EFL learners’ acquisition of collocations through ChatGPT and movie language

This study investigates the potential of generative artificial intelligence (AI), specifically ChatGPT\r\nprimed with data from the American Movie Corpus (AMC), to support the acquisition of high-\r\nfrequency spoken collocations in a general English context. Thirty-four B2-level Italian university\r\nstudents were randomly assigned to guided AI, unguided AI, or non-AI control conditions and\r\ncompleted a ten-week task-based language teaching (TBLT) programme. Eight target collocations\r\n(e.g., good thing, little problem, get home) were selected based on frequency and statistical\r\nassociation measures (t-score > 2; MI > 3) in the AMC. Learner output was analysed using a linear\r\nmixed-effects model to compare collocation frequency at pretest, posttest and delayed posttest.\r\nWhile no statistically significant differences emerged between instructional conditions, the findings\r\npoint to important trends that suggest potential benefits of AI-mediated tasks. These results are\r\ninterpreted with caution, given small, unequal groups and a conservative bigram operationalisation\r\nthat may underestimate more flexible patterns. While the integration of corpus-informed input into\r\nAI-mediated tasks remains promising, sustained exposure, stronger focus-on-form, and broader\r\npattern coding may be required to detect gains.