Discovering Inequalities Through Contradictions: An Activity Theory Analysis of Generative AI Integration in Doctoral Supervision

• In Event: Expectations and Experiences in Advising, Mentoring, and Supervising in Graduate Education

Abstract

Generative artificial intelligence (GenAI) tools like ChatGPT have been increasingly adopted by doctoral students. However, stakeholders involved in doctoral supervision may have conflicting reactions toward GenAI integration, raising questions about educational equality. This study used Activity Theory (AT) to analyze the contradictions and inequalities arising from GenAI integration in doctoral education. Using a qualitative approach, we collected data from interviews with 20 doctoral students and analyzed it with deductive and inductive content analysis. The study identified contradictions within the elements of tool, rule, subject, community, and division of labor, and revealed disciplinary, regulatory, and access inequalities. The findings extend the application of AT in GenAI integration in doctoral supervision, uncover educational inequalities, and suggest future doctoral education practices.

Authors

• Sichen Lai, Chinese University of Hong Kong

• Yun Dai, Chinese University of Hong Kong