Rethinking Jigsaw Method with Partially Engaged AI

Abstract

The Jigsaw method is a collaborative learning approach that benefits students' accountability and teamwork achievement. However, the Jigsaw method causes students' cognitive overload, as the participants need to engage in both the expert group and the home group, and have to deal with different tasks. In this study, we propose an AI partially engaged strategy in which AI plays a role as a professional peer for responding to specific questions and constructing knowledge with students in the expert group. This study was conducted in a course with 38 university students, and the results indicate that students constructed related knowledge by showing significantly higher post-test results than the pre-test. This result suggests that in future developments of this topic, quantitative analysis of students’ cognitive load can be conducted.