Supervision Skills of Supervisors in AI-enhanced Environments: Perspectives on Postgraduate Supervision

Abstract

The use of AI by postgraduate students quickly changes supervisory relationships and requires new supervisory skills. This study examines the fundamental supervisory abilities needed to manage postgraduate students who integrate AI tools into their research work. It employs a qualitative research method based on exploratory phenomenology within an interpretive research paradigm to investigate supervisors’ subjective experiences and perspectives in AI-integrated supervision environments. Ten purposively selected supervisors with experience in AI-enhanced settings provided data through semi-structured interviews. An analysis of the interview transcripts using thematic methods revealed consistent patterns and themes regarding supervisory competencies. Supervisors need to cultivate critical evaluation skills to identify students’ overdependence on AI systems and learn how to detect AI-generated material that lacks originality by interpreting underlying meanings. Students require guidance from supervisors in learning essential research techniques, such as literature searching and correct source attribution, to uphold academic integrity. The study emphasises the importance of supervisors mandating students to record their research steps and participate in evaluative discussions to test their understanding and ethical use of AI. Supervisory responsibilities must incorporate AI tools while simultaneously promoting independent critical thought and ethical principles. The study proposes specialised training programmes for supervisors to enhance their AI literacy and evaluation skills while also creating clear ethical guidelines for AI use in postgraduate research. Future research should investigate how AI integration affects supervisory relationships over time and develop scalable supervisor training frameworks suitable for various academic fields and institutional settings.