Tipo: capítulo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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Providing personalized, detailed feedback at scale in large undergraduate STEM courses remains a persistent challenge. We present an empirically evaluated practice exam system that integrates AI generated feedback with targeted textbook references, deployed in a large introductory biology course. Our system specifically aims to encourage metacognitive behavior by asking students to explain their answers and declare their confidence. It uses OpenAI's GPT-4o to generate personalized feedback based on this information, while directing them to relevant textbook sections. Through detailed interaction logs from consenting participants across three midterms (541, 342, and 413 students respectively), totaling 28,313 question-student...

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