The impacts and tensions of generative AI on doctoral students’ supervisory and peer dynamics: An activity theory analysis

Doctoral students are increasingly adopting generative artificial intelligence (GenAI) tools in their daily academic activities. However, it remains unclear how GenAI influences doctoral training, particularly in terms of supervisory and peer interactions within PhD programmes. This qualitative study investigated the impact of GenAI adoption on doctoral students’ interactions with supervisors and peers within their immediate academic environments. Guided by activity theory as the theoretical framework, we conceptualise doctoral training as an academic activity system mediated by GenAI tools within specific social and cultural contexts. Through in-depth interviews and thematic analysis, this study examined the experiences of 20 doctoral students who were early adopters of GenAI at an Australian university between June and August of 2023. Two key tensions emerged from the analysis: first, the tensions arising from the dual nature of GenAI tools, characterised by their affordances and inherent limitations; second, the conflict between productivity-oriented research practices and traditional academic norms. These tensions further triggered interpersonal tensions over differing attitudes or stances towards GenAI and conflicting expectations regarding supervisory responsibilities among students, supervisors and peers. The findings reflect evolving power relations, interpersonal dynamics and academic socialisation in the context of GenAI integration. This study offers theoretical and empirical insights for rethinking doctoral supervision and training in the GenAI era. Copyright © 2025 Sichen Lai, Suya Liu, Yun Dai, Cher Ping Lim, Ang Liu.