🔬 Research & Scholarship

Pillar 4: Research and Scholarship addresses the extent to which Artificial Intelligence is integrated into an institution’s research culture, both as a tool and as a field of inquiry. This pillar is organised around three key indicators: AI in research practice, the scholarship of AI in practice, and AI-focused academic research. These indicators assess not only how AI is being applied to enhance research productivity and insight, but also how institutions critically engage with AI through scholarly analysis and knowledge generation.

The first indicator, AI in research practice, considers how institutions incorporate AI tools to improve the research process itself. This could include the use of AI in areas such as data analysis, literature review, hypothesis generation, and modelling. The framework measures whether AI is actively used in the conduct of research, improving efficiency and enabling new forms of inquiry across disciplines.

The second indicator, scholarship of AI in practice, focuses on how institutions study their own adoption of AI. This involves taking a scholarly approach to understanding the institutional impact of AI on teaching, learning, research, and operations. It includes critical inquiry into the ethical, educational, and social implications of AI integration within the institution.

The third indicator relates to AI-focused research output, assessing whether the institution is producing original research in the field of AI itself. It also considers the extent to which AI is a topic of research within other disciplinary domains and examining how AI is transforming practices and epistemologies across disciplines.