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

Checked 30 Sept 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.

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. Implications for practice or policy: GenAI integration in doctoral education requires redefining the roles, responsibilities and relationships between students, supervisors and peers. Doctoral supervision should transition towards a more collaborative approach, emphasising co-learning, open communication and human-AI collaboration. Doctoral programmes need to develop clear institutional policies and structured training programmes for supervisors and students to facilitate effective GenAI use and minimise related tensions.

• Across two independent studies of doctoral supervision, hidden GenAI use emerges as the central relational problem: Boyd & Harding 2025 find students "(often covertly) using GenAI tools to support their emotional and discipline specific development," while Lai et al. 2025 trace "conflicting expectations regarding supervisory responsibilities among students, supervisors and peers"—and both conclude that supervision must become transparent and collaborative rather than prohibitive. Trace →

• Supervision and doctoral educationanchor