AI-Augmented Doctoral Education: Designing Pedagogical and Qualitative Research Frameworks for Authentic Knowledge Creation in the Age of Intelligent Technologies

Research objectives：

This project aims to develop both conceptual and empirical frameworks for AI-augmented doctoral education that maintain intellectual rigour, originality, and scholarly identity while utilising intelligent technologies. It will also explore innovative qualitative methodologies to support students conducting research in this post-digital era.

Specific objectives and themes for possible research and publications articles may include:

1: To investigate how AI tools are reshaping doctoral research practices, supervision processes and knowledge production.
2: To explore how qualitative research methodologies are evolving in response to AI-supported analysis, writing and synthesis tools.
3: To develop a pedagogical framework for AI-augmented doctoral supervision that ensures authentic scholarly development.
4: To identify risks associated with AI-assisted doctoral work, including superficial synthesis, loss of scholarly voice, and epistemic dependency.
5: To design a doctoral curriculum model integrating AI literacy, research integrity and advanced thinking skills.
6: To develop a qualitative framework for evaluating doctoral originality in AI-supported research environments.
7: To propose institutional strategies for redesigning doctoral education for the AI era.
8: To employ AI as a co-supervisor for doctoral students

Keywords：

• AI in doctoral education
• Doctoral pedagogy
• AI-augmented supervision
• Qualitative research innovation
• Digital scholarship
• Research integrity
• Doctoral curriculum design
• AI literacy
• Academic identity formation
• Higher education transformation

Research Design:

• Qualitative multi-phase design combining qualitative case studies, design-based research, participatory research, and action research elements.
• Data sources could include doctoral candidates, supervisors, institutional leaders, doctoral programme documents, AI usage practices, and Doctoral assessment criteria.
• Methods could include interviews, focus groups, listening circles, document analysis, reflective journals, AI-interaction analysis, and workshop observations.
• Analytical approaches such as thematic analysis, reflexive qualitative analysis, grounded theory elements, and design framework development.

Key expected findings:

Identification of emerging AI-doctoral research practices, such as:

• A typology of AI usage in doctoral work
• A model of AI-augmented supervision
• A framework for protecting doctoral originality
• A qualitative model for evaluating doctoral thinking development
• A redesigned doctoral curriculum structure
• A conceptualisation of the AI-native doctoral journey

Expected Outcomes：

Academic: Peer-reviewed publications

 

Co-academic leads：