Tag: emergent-scholarship
6 items with this tag.
Research taste
Research taste is the cultivated capacity to recognise which problems are worth pursuing, which collaborators will amplify your work, and which under-explored areas have genuine leverage — before you can fully prove any of those judgements.
A new home for my writing
Introducing my new platform for writing on AI in health professions education. It explains what's happened to usr/space and Head Space, what this newsletter will cover going forward, and links to my most recent presentations and posts.
The quality of the challenge: AI as a thinking partner
Most discussions of AI in writing focus on output. This post describes using AI as a thinking partner while revising a theoretical framework paper for journal submission, asking it for critique, arguing back where it was wrong, and testing the draft against my own voice. The exchange changed the paper's structure and how it describes its methodology, and showed that the value lay in the quality of the challenge.
Building an AI workflow for academics with structured documentation
Most advice on building an AI workflow for academics concentrates on writing better prompts. After several weeks of restructuring years of scholarly output with Claude Code, I've found the leverage lies in the structured context around the model — documentation that works as external memory, a cycle of testing rules and locking in the ones that hold, and the discipline of making implicit connections explicit. The same applies to institutions, where AI tools are only as reliable as the information architecture they're given.
Boyer's model of scholarship
A multidimensional framework for scholarship spanning discovery, integration, application, and teaching.
A theoretical framework for integrating AI into health professions education
Health professions education faces a fundamental challenge: graduates are simultaneously overwhelmed with information yet under-prepared for complex practice environments. This essay introduces a theoretically grounded framework for integrating AI into health professions education that shifts focus from assessing outputs to supporting learning processes. Drawing on social constructivism, critical pedagogy, complexity theory, and connectivism, six principles emerge — dialogic knowledge construction, critical consciousness, adaptive expertise, contextual authenticity, metacognitive development, and networked knowledge building — to guide AI integration in ways that prepare professionals for the complexity and uncertainty of contemporary healthcare practice.