Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

Large language models (LLMs) are increasingly used for automated essay scoring, yet their underlying scoring mechanisms remain insufficiently understood. This study systematically compared the scoring behavior of three LLMs (Qwen, GPT, and Gemini) with human raters on English essays written by non-native learners. Sixteen textual features were analyzed to compare score alignment, feature weighting, subgroup consistency, and feature interactions. Results showed strong overall alignment but distinct feature weighting patterns between the LLMs and human raters. Specifically, the LLMs placed greater emphasis on grammatical accuracy, lexical sophistication, and syntactic complexity, indicating a...

Ver ficha completa