The integration of Artificial Intelligence (AI) in academic writing is transforming the way thesis writing is approached in universities. With AI, students can now complete tasks such as literature review, drafting, editing, and textual analysis more efficiently. This has led to a significant reduction in the time required to produce a complete thesis. As a result, universities are being urged to reconsider the traditional duration of study and the structure of thesis writing.
The use of AI in thesis writing has raised questions about the relationship between writing time, research duration, and institutional time. Writing time refers to the period required to produce a complete thesis, while research duration includes the time needed to define and refine the research problem, obtain ethical approval, and collect data. Institutional time, on the other hand, refers to the formal period allocated to a degree. AI may compress writing time, but it may not have the same impact on research duration and institutional time.
The changing research environment brought about by AI also gives supervision a broader intellectual function. With AI supporting routine tasks, supervisors can focus on conceptual clarification, theoretical development, and methodological reasoning. The relationship between student and supervisor can shift from correcting routine tasks to sustained engagement with the ideas that give research its significance. This development requires universities to adopt a more differentiated approach to the relationship between competence, research design, and candidature.
Bloom’s taxonomy provides a useful framework for this transition. AI can support activities associated with remembering, understanding, organising, and applying knowledge. Postgraduate education can consequently place greater emphasis on analysis, evaluation, and knowledge creation. This shift in focus can lead to a more comprehensive assessment of a student’s abilities and knowledge.
One possible solution is to embed research more deliberately within postgraduate coursework. Advanced courses could address theory construction, epistemology, research design, and scholarly communication. These courses could be connected to students’ developing research problems, allowing them to test concepts and methods before and alongside thesis writing. This approach can help to reduce the weighting of thesis writing and provide a more balanced assessment of a student’s abilities.
Another possible solution is to reconsider the role of comprehensive examinations. A well-designed comprehensive examination can assess a student’s understanding of major research theoretical traditions, ability to compare competing paradigms, and capacity to justify methodological choices. This approach can provide a more direct assessment of theoretical command and intellectual breadth.
The implications of AI on thesis writing will differ across levels of study. Undergraduate students should demonstrate a sound understanding of research and the ability to apply established concepts. Master’s students should show stronger theoretical command, independent analysis, and methodological competence. Doctoral students should be expected to problematise concepts, interrogate paradigms, and make an original contribution to knowledge.
Key points
- AI is pushing universities to redefine learning outcomes in thesis writing.
- Universities should adopt a more differentiated approach to the relationship between competence, research design, and candidature.
- AI can support activities associated with remembering, understanding, organising, and applying knowledge, allowing postgraduate education to place greater emphasis on analysis, evaluation, and knowledge creation.
