Doctoral Research Programme
Research
My research programme, its guiding themes and the methods I use to pursue it.
Doctoral research
The Role of Generative AI and Agency in the Epistemological Foundations of Contemporary Pedagogy
My doctoral research examines how generative AI is changing the epistemological foundations of teaching and learning. When AI systems can draft, summarise, explain and argue, long-standing assumptions about what it means to know, to author and to demonstrate learning are placed under pressure.
The project asks how epistemic agency — a learner’s capacity to form, justify and take responsibility for their own knowledge — is preserved, redistributed or eroded when cognitive work is shared with AI systems. It connects theoretical work on distributed cognition and human–AI co-agency with the practical questions universities face around assessment design, academic integrity and AI governance. Its research focus is investigating the impact of generative AI on knowledge formation, learning processes and educational agency in contemporary pedagogy.
Institution: Charles Darwin University, Australia Supervisor: Dr Jon Mason, Associate Professor in Education (e-Learning), Faculty of Arts & Society Status: Ongoing
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Research themes
Seven connected themes structure my doctoral work and downstream projects.
Epistemic Agency
How learners retain the capacity to form, justify and take responsibility for their own knowledge when AI shares the cognitive work.
Distributed Cognition
Understanding learning as cognition spread across people, tools and AI systems, and what that means for attribution of intellectual work.
Human–AI Co-Agency
Models of shared agency in which humans and AI systems jointly produce work, while humans remain accountable for meaning and judgement.
Cognitive Offloading
When delegating thinking to AI supports learning, and when it hollows out the capabilities education is meant to build.
Evaluative Judgement
The capability to appraise the quality of work — one's own, others' and AI-generated — as a central goal of AI-era assessment.
Academic Integrity
Rethinking authorship, honesty and fair assessment decisions when generative AI is part of ordinary study practice.
AI Governance in Higher Education
How universities translate principles about AI into workable policy, and whether those policies produce consistent, fair decisions.
Research methods
The methodological toolkit I use across projects, matched to the kind of question being asked.
Systematic Literature Review
Structured, protocol-driven synthesis of research evidence with explicit search, screening and appraisal criteria.
Scoping Review
Mapping the breadth of an emerging field — key concepts, evidence types and gaps — where the literature is fast-moving and heterogeneous.
Qualitative Coding
Systematic labelling of textual data with structured codebooks, supporting transparent and auditable analysis.
Thematic Analysis
Identifying, reviewing and defining patterns of meaning across qualitative data sets.
Discourse Analysis
Examining how language in policy and public texts constructs AI, students, integrity and responsibility.
Conceptual Synthesis
Integrating theory across philosophy of education, cognitive science and AI ethics into usable frameworks.
Policy and Document Analysis
Systematic comparison of institutional policies, procedures and guidance documents against structured analytic frameworks.