Effects of Incorporating an Expert Decision-making Mechanism into Chatbots on Students’ Achievement, Enjoyment, and Anxiety

Abstract

In traditional instruction, teachers generally deliver the content of textbooks to students via lectures, making teaching activities lack vibrancy. Moreover, in such a one-to-many teaching mode, the teacher is usually unable to check on individual students’ learning status or to provide immediate feedback to resolve their learning problems. Chatbots provide an opportunity to address this problem. However, conventional chatbots generally serve as information providers (i.e., providing relevant information by matching keywords in a conversation) rather than as decision-making advisors (i.e., using a knowledge-base with a decision-making mechanism to help users solve problems). Thus, this study proposes an expert decision-making-based chatbot to facilitate individual students’ construction of knowledge during the learning process. A quasi-experiment was conducted to compare the differences in the performances and perceptions of students using the expert decision-making-based chatbot (EDM-chatbot) and the conventional chatbot (C-chatbot) in the activities of a geography course. One class of 35 students was the experimental group, using the EDM-chatbot. The other class of 35 students was the control group, using the C-chatbot. The results of the study showed that the EDM-chatbot combined with expert decision-making knowledge significantly improved students’ learning achievement and learning enjoyment as well as reducing their learning anxiety, showing the value of the proposed approach.

Keywords

• artificial intelligence in education
• expert knowledge
• decision tree
• chatbot
• interactive learning system