Reshaping business education: An activity theory analysis of AI teaching assistants

Abstract

This study examines how an AI-powered teaching assistant was integrated into business courses at the Auckland University of Technology to address the limitations of traditional, large-scale teaching models. Business education increasingly demands flexible and individualised learning support, yet empirical evidence on the pedagogical value of purpose-built AI tools remains limited. Guided by Activity Theory and using an explanatory sequential mixed-method design, this research analysed survey data, semi-structured interviews, and reflective field notes to explore how AI mediated learning, shaped educator and student roles, and influenced academic outcomes. The findings indicate that NF AI enhanced engagement, efficiency, and self-directed learning through instant formative feedback, while also easing lecturer workload. However, issues such as inconsistent feedback, limited linguistic adaptability, and institutional integration challenges revealed systemic tensions in AI adoption. The study extends Activity Theory by identifying two new analytical constructs: the community-embedded artefact, where AI acts both as a mediating tool and a social participant in the learning environment, and the spatial misalignment contradiction, highlighting infrastructural frictions between local institutions and external AI providers. These insights contribute to a deeper understanding of AI’s pedagogical implications in business education, emphasising the importance of ethical, context-specific integration and sustained human oversight to ensure learning remains meaningful, equitable, and pedagogically grounded.