Advancing asynchronous pre-class learning in flipped classrooms: Generative AI companions in business ethics

Flipped classroom pedagogy, primarily focused on in-person classroom settings, emphasizes pre-class independent learning where students engage asynchronously with online materials and self-assessments. However, traditional pre-class learning methods, such as watching pre-recorded instructional videos and completing multiple-choice self-assessments, often fail to prepare students for in-person peer debates, which are crucial in business ethics education for developing higher-order thinking and ethical reasoning. This study addresses this issue by developing a generative AI learning companion system tailored for an asynchronous business ethics course. The system enables students to practice ethical debates with AI partners during the pre-class phase, improving their readiness and confidence for in-person peer debates. We implemented this system with sixty-two third-year students from a national university in Taiwan, divided into experimental and control groups. Our findings demonstrate that the AI-supported debate method significantly enhanced students' pre-class preparation and ethical learning confidence without increasing their autonomous learning task load. This approach offers a novel solution for flipped classroom pedagogy, particularly in courses requiring intensive peer interaction such as business ethics.