Generative Artificial Intelligence in Doctoral Supervision: Ethical Boundaries, Feedback Practices, and Supervisory Relationships

Generative artificial intelligence is increasingly being adopted in doctoral education to support writing, literature synthesis, translation, feedback, research planning, and administrative work. While emerging scholarship has examined generative AI in higher education, academic integrity, and student writing, its specific implications for doctoral supervision remain comparatively underexplored. This review argues that generative AI should not be understood merely as a technical aid, but as a socio-technical force that may reshape ethical boundaries, feedback practices, doctoral student agency, and supervisory relationships. Drawing on literature from doctoral education, supervision studies, academic writing, feedback literacy, and AI-mediated learning, the review examines three interrelated thematic areas of AI-augmented doctoral supervision. First, it considers ethical issues related to authorship, originality, academic integrity, transparency, privacy, confidentiality, bias, data protection, and overdependence. Second, it analyses how generative AI may influence feedback preparation, interpretation, timing, and dialogic use in doctoral writing and research development. Third, it explores implications for supervisory relationships, particularly trust, power, responsibility, emotional support, and autonomy. The review also discusses institutional implications, including AI literacy, supervisor development, doctoral policy, and responsible use guidelines, and positions generative AI as a human-centred support mechanism rather than a substitute for supervisory judgement, ethical responsibility, or relational mentoring.