Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

This exploratory study investigates the performance of three widely-used and freely available large language models (LLMs)— ChatGPT (GPT-3.5 Turbo), Gemini, and Copilot—in answering multiple-choice questions (MCQs) categorized by cognitive complexity based on the revised Bloom's Taxonomy. Although MCQs offer a structured, efficient, and scalable method for evaluation, a gap exists in the literature on how LLMs handle varying cognitive levels, particularly in a business course at higher education. Understanding LLM performance in this context is crucial, as students increasingly use LLMs for searching answers but also to receive tailored and scaffolded responses for an interactive and...

Ver ficha completa