Enhancing Teaching Quality Through LLM: An Experimental Study on Prompt Engineering

This study explores the application of Large Language Models Artificial Intelligence (LLM AI) in the assessment of course teaching quality, aiming to overcome the limitations of traditional teaching evaluation. Taking the experimental courses in School G as an example, we propose CORE, a framework to support generating feedback from student assessment during the course. Through the Solomon four-group design experiment, it validates the significant effectiveness of the teaching quality evaluation feedback generated by LLM in enhancing teachers' teaching quality. This feedback can help teachers improve teaching strategies, boost teaching effects, effectively make up for the limitations of traditional teaching assessment methods, and offer a new perspective and tool for teaching quality evaluation.