research-summary

Working title: After Intelligence - building possibility spaces for learning and making with and through generative systems

This research works with LLMs both as material, actively shaping the experience of learning and making, and as infrastructure, building the environments within which that experience can be investigated.

The project begins from a concern that generative AI often compresses process, producing polished outputs that can obscure the messier, more reflective aspects of learning. Rather than critique this tendency from the outside, I am interested in exploring whether different relationships with these systems might be possible, and what they might look like in practice. The core artefact is PhD-Live, a public digital garden that keeps knowledge-in-process visible as a research proposition. Built alongside it, a suite of local AI tools (a Supervisor Bot, Study Companion, and a knowledge layer that feeds my own prior thinking back into the conversation) extends that investigation into the practice itself. Together they explore what more exploratory, personalised and ethical relationships with AI might look like in practice, pushing against the efficiency and productivity framing that dominates current discourse. The gaps and failures of that infrastructure are as much the research as the working parts.

My position as both learner and teacher shapes the investigation, and the work has a strong autoethnographic thread. If the central concern is what gets erased when AI mediates knowledge work, then the methodological response is to make that process visible and available for reflection. A commitment to liveness runs through the research - keeping knowledge public whilst still forming, as research artefact rather than purely a record.

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27/05/2026 - **Working title: After Intelligence - building possibility spaces for learning and making with and through generative systems** **Working title - After Intelligence: building possibility spaces for learning and making with and through generative systems** This research works with LLMs both as material, actively shaping the experience of learning and making, and as infrastructure, building the environments within which that experience can be investigated. This research works with LLMs as material, actively shaping the experience of learning and making, and as infrastructure, building the environments within which that experience is investigated. The project begins from a concern that generative AI often compresses process, producing polished outputs that can obscure the messier, more reflective aspects of learning. Rather than critique this tendency from the outside, I am interested in exploring whether different relationships with these systems might be possible, and what they might look like in practice. The core artefact is PhD-Live, a public digital garden that keeps knowledge-in-process visible as a research proposition. Built alongside it, a suite of local AI tools (a Supervisor Bot, Study Companion, and a knowledge layer that feeds my own prior thinking back into the conversation) extends that investigation into the practice itself. Together they explore what more exploratory, personalised and ethical relationships with AI might look like in practice, pushing against the efficiency and productivity framing that dominates current discourse. The gaps and failures of that infrastructure are as much the research as the working parts. The work begins from a specific problem: generative AI in learning tends to collapse process, producing polished and swift outputs that compress thinking, erasing the mess where learning happens. Rather than critique this tendency from the outside, I am asking whether a different relationship is possible, and building toward an answer. The core artefact is PhD-Live, a public digital garden that keeps knowledge-in-process visible as a research proposition. Built alongside it, a suite of local AI tools (a Supervisor Bot, Study Companion, and a knowledge layer that feeds my own prior thinking back into the conversation) extends that investigation into the practice itself. Together they probe what it means to build a genuine thinking partner rather than a generic tool centred on optimisation. The gaps and failures of that infrastructure are as much the research as the working parts. My position as simultaneously learner and teacher shapes the investigation and the work has an autoethnographic central thread. If the problem is what gets erased when AI mediates knowledge work, the methodological response is to centre that process. A commitment to liveness runs through the research - keeping knowledge public whilst still forming, as research artifact rather than purely a record. My position as both learner and teacher shapes the investigation, and the work has a strong autoethnographic thread. If the central concern is what gets erased when AI mediates knowledge work, then the methodological response is to make that process visible and available for reflection. A commitment to liveness runs through the research - keeping knowledge public whilst still forming, as research artefact rather than purely a record.