Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

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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...

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

To address the complexity and high costs of developing listening tests for test-takers of varying proficiency levels, this study investigates the capabilities of an OpenAI's large language model, ChatGPT 4, in developing listening assessments. Employing prompt engineering and fine-tuning of prompts, the study specifically focuses on creating listening scripts and test items using ChatGPT 4 for test-takers across a spectrum of proficiency levels (academic, low, intermediate, and advanced). For comparability, the 24 topics of these scripts were selected from topics found in academic listening tests. We conducted two types of analyses to evaluate the quality of the output. First, we performed...

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