Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Recent advances in large language models (LLMs), notably GPT models, have introduced new possibilities for automatic item generation (AIG) through prompt engineering. Although iterative prompt refinement can yield measurable improvements, this process eventually plateaus, as outputs remain inconsistent or misaligned with assessment constructs. This study reports an experimental comparison of prompting and fine-tuning to advance AIG for L2 listening assessment. First, we employed prompting and refined the instruction design over three successive iterations to determine an optimized prompt. We then fine-tuned GPT-4.1 using the same optimized prompt, holding prompt design constant to isolate...

Ver ficha completa