AI-powered Tools for Doctoral Supervision in Higher Education: A Systematic Review

Chee Ling Thong, Zainab Atallah (), Shayla Islam (), WeiLee Lim () and Aswani Kumar Cherukuri ()
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Chee Ling Thong: , †Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Wilayah Persekutuan, Malaysia
Zainab Atallah: , †Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Wilayah Persekutuan, Malaysia
Shayla Islam: , †Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Wilayah Persekutuan, Malaysia
WeiLee Lim: ��Graduate Business School, UCSI University, Kuala Lumpur, Wilayah Persekutuan, Malaysia
Aswani Kumar Cherukuri: �School of Computer Science Engineering & Information Systems, Vellore Institute of Technology, Vellore – 632014, India

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 02, 1-27

Abstract: Artificial intelligence (AI)-powered tools are used to aid the learning and teaching process in higher education. AI technology aims to assist doctoral co-supervision through the model of humanised collaboration. It is discovered that there is a lack of literature review integrating generative AI (GenAI) with doctoral co-supervision processes. This paper investigates how GenAI facilitates the doctoral co-supervision process, including types of AI used, AI in education (AIED) components integration and the extent to which AI applications are useful in doctoral co-supervision. Four research questions posed have guided the study. The findings show AI to be supportive of personalised instruction and assessment and to be used as a collaborative tool. Furthermore, machine learning algorithms with a predictive nature were of immense aid in personalised advice. Nevertheless, the experience of the fusion of AI and mobile technologies in academic mentoring is relatively scarce in empirical studies. It was found that extended case studies and consumer experience were lacking in this area. Even though the potential benefits were clarified, a comprehensive assessment of the dynamic effects called for by more robust empirical investigations is required, considering further constraints. This paper summarises that future investigations and research are still needed.

Keywords: Artificial intelligence; co-supervision; AI-based mobile applications; machine learning; quality education (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219649225300013

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