Improving students’ learning performance and summarization ability through a Generative AI-enabled Chatbot - Anna Y. Q. Huang, Chien-Chang Lin, Sheng-Yi Su, Ruo-Xuan Yen and Stephen J. H. Yang (Educational Technology & Society)
Previous studies have demonstrated that summarization activities effectively enhance students’ reading comprehension and learning performance. With the ongoing surge in the application of artificial intelligence in education, this study introduces a chatbot with generative artificial intelligence (GenAI) to assist students in reviewing Python programming concepts by refining the summary after class. Consequently, this study aims to assess the efficacy of the proposed a GenAI-enabled chatbot in enhancing the summarization ability and learning performance of novice programmers in college. Experimental results reveal that, the GenAI-enabled chatbot not only significantly improves students’ programming coding performance but also significantly improves their summarization ability. It is noteworthy that students’ summarization proficiency is not only elevated from the outset but also consistently progresses until the end. This study also identified that understanding the Python programming concepts of input/output operations and lists serves as predictors for learning performance in programming concept and coding, respectively. This emphasizes the significance of fundamental programming concept and a nuanced understanding of advanced data types in programming learning.