aceExploring GenAI affordance in EFL continuation task instruction for developing Chinese high school students’ writing complexity, accuracy and fluency
Abstract The role of generative artificial intelligence (GenAI) affordance in specific task teaching, particularly the continuation writing task, remains insufficiently theorized and empirically underexplored. To bridge this research gap, a repeated-measures intervention study was conducted within the framework of Dynamic Systems Theory (DST) to investigate the implementation of GenAI-assisted continuation task instruction and its associations with the development of English as a Foreign Language (EFL) students’…
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In a 9-week repeated-measures study, 25 Chinese high school EFL students completed five continuation writing tasks at two-week intervals while receiving GenAI-assisted instruction, producing 125 samples analyzed with generalized estimating equation analysis under Dynamic Systems Theory.
The documented time effect and three-phase, nonlinear CAF patterns suggest GenAI affordance may shape L2 writing development in this task type, but the small, single-context sample and short duration leave open whether effects persist, transfer to other tasks, or generalize beyond these learners.
- Repeated-measures intervention study framed by Dynamic Systems Theory (DST).
- 25 Chinese EFL high school students over 9 weeks with five measurement points at two-week intervals.
- 125 writing samples analyzed via generalized estimating equation (GEE) analysis.
- Nonlinear progressions of complexity, accuracy, and fluency exhibited distinct patterns under GenAI-mediated instruction.
Chinese high school EFL students showed a significant time effect and three-phase trajectory in writing complexity, accuracy and fluency during GenAI-assisted continuation task instruction.
The rundown
The study implemented five continuation writing tasks at two-week intervals over nine weeks, collecting 125 samples from 25 participants and analyzing them with GEE to identify model effects within a DST framework.
Results reported a significant main effect of Time and distinct nonlinear trajectories for complexity, accuracy and fluency, which the authors interpret as patterns observable under GenAI-assisted instruction.
Sources
- Peer-reviewedDiscover Computing2026-06-23
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