5 items with this tag.
Most discussions of AI in writing focus on output. This post describes a different experience—using AI as a thinking partner to challenge my choices and claims during a writing session.
Most conversations about AI focus on what it produces. This post describes what an AI workflow for academics actually looks like in practice — building structured context through documentation, iteration, and judgement that makes AI collaboration increasingly effective over time. Drawing on several weeks of restructuring scholarly output with Claude Code, I describe the iteration cycle, the role of documentation as external memory, and what the process reveals about the relationship between explicit information architecture and productive AI collaboration.
Academic culture has converged on the peer-reviewed journal article as the default unit of scholarly output, creating a hierarchy that excludes many valuable forms of intellectual work. This post makes the case for essays as a legitimate form of scholarship—not as a lesser alternative to empirical research, but as a distinct mode that enables exploration, synthesis, and engagement with audiences that traditional publishing cannot reach. Drawing on Boyer's model of scholarship, it argues for a more generous conception of what counts as scholarly contribution.
The predominant AI interface paradigm — text boxes and chronological chat histories — reproduces a deeply embedded cognitive metaphor that misaligns with how professional expertise develops. Drawing on Lakoff and Johnson's container schema, this essay traces how a single organising metaphor has been uncritically reproduced across physical, digital, and AI-mediated learning environments, artificially enclosing knowledge that practitioners must mentally reintegrate. Rather than proposing to replace bounded learning spaces, this essay explores graph-based learning environments as an alternative paradigm where bounded spaces become visible communities within a navigable network. AI serves as both conversational partner and network weaver, with conversations spatially anchored to relevant concepts rather than isolated in chronological chat histories. This reconceptualisation — from enclosure to constrained traversal — suggests possibilities for AI-supported learning environments that better develop the integrative capabilities defining professional expertise.
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.