%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Lin-Zucker, M.
%A Bellassen, J.
%A Zucker, Jean-Daniel
%T Prompting large language models for CEFR-EBCL-aligned chinese L2 learning : an empirical study of sinographic constraint compliance
%D 2026
%L fdi:010097586
%J Journal of Technology and Chinese Language Teaching
%@ 1949-260X
%K Chinese as a Second Language ; Large Language Models ; Prompt Engineering ; CEFR-EBCL Alignment ; Sinographic Constraints
%K CHINE
%M ISI:001819166400001
%N 1
%P 1-29
%U https://www.documentation.ird.fr/hor/fdi:010097586
%> https://www.documentation.ird.fr/intranet/publi/2026-08/010097586.pdf
%V 17
%W Horizon (IRD)
%X Large Language Models (LLMs) are increasingly used in Chinese as a Second Language (L2) learning, yet their ability to comply with pedagogical constraints specific to the Chinese writing system remains underexplored. This study examines whether system prompts aligned with the CEFR-EBCL framework enable LLMs to generate learner-facing Chinese texts that respect sinographic thresholds at the A1, A1+ and A2 levels. We conducted controlled experiments using two models (GPT-4.1 and GPT-4.1-mini) across ten EBCL-related written tasks. Prompt conditions with and without explicit character lists were compared. Model outputs were automatically analyzed to quantify instruction deviation, defined as the proportion of characters outside the target EBCL set. Results indicate that including explicit character lists significantly reduces out-of-list character production at the A1 and A1+ levels, particularly with GPT-4.1. At the A2 level, this effect becomes marginal. These findings provide empirical evidence on the pedagogical value and limits of prompt-based control of ChatGPT outputs for CEFR-EBCL-aligned Chinese L2 learning.
%$ 106 ; 020