Chatbots for active learning: A case of phishing email identification.

Abstract

Chatbots represent a promising approach to provide instructional content and facilitate active learning processes. However, there is a lack of knowledge as how to design chatbot interactions for active learning. In response to this knowledge gap, we conducted an experimental study (n = 164) comparing four modes for providing instructional content in chatbots, with varying demands for cognitive engagement. The four modes – passive, active, constructive, and interactive – were based on the ICAP framework of active learning. The learning content concerned identification of phishing emails and the four modes were distinguished by how the participants were invited to engage with the content during their chatbot interaction. The ICAP modes of higher cognitive engagement required participants to spend more time on the interaction and led to perceptions of higher subjective learning outcome. However, the effects of the different ICAP modes were not found to be significantly different in terms of user engagement, social presence, intention to use, or objective learning outcomes. The study represents an important first step towards understanding the design of chatbots for active learning.