Artificial intelligence in doctoral training: Bridging gaps, easing burdens

In an era when artificial intelligence is reshaping knowledge, doctoral education faces structural and institutional pressures that jeopardise retention. This systematic review, complemented by a reflective thematic synthesis, identifies early-stage researchers' main barriers and examines how AI's promises might address them. Following PRISMA, we analysed 92 articles using thematic synthesis alongside descriptive frequency mapping. Five major barrier categories emerged: mental health and well-being; academic supervision; personal factors (including work–life balance, financial constraints, and motivation); academic skills; and institutional factors (including work organisation). Findings indicate AI offers support through personalised learning, intelligent assistance, and task automation. The study informs educational leaders, supervisors, and researchers pursuing hybrid supervision, evidencing AI's transformative potential in advanced academic training.