Over the past few days, I conducted a small experiment in hybrid concept mapping. The goal was straightforward: take a loose cluster of existing notes from my TiddlyWiki and transform them into a structured map that could withstand scrutiny. These notes (42 of them) were all tagged "LS-Cognitive". The LS stands for Learning Sprint.
The working title of the resulting draft map is:
Maintaining Cognitive Agency and Brain Capital Under Conditions of AI Amplification
The process unfolded in stages.
First, I extracted key concepts from existing tiddlers and generated a provisional graph structure using AI (Ace Knowledge Graph, accessible as an app within ChatGPT Plus). This initial pass surfaced nodes such as Cognitive Agency, Cognitive Control, Cognitive Offloading, Human–Machine Symbiosis, Brain Capital, and Brain Economy. The resulting structure was technically coherent but conceptually uneven. This is the first time I manage to do something like this combining my TiddlyWiki content with mapping so I was impressed no matter how imperfect the map was.
The Ace Knowledge Graph performed three useful operations:
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Entity Extraction
It identified recurring conceptual terms across the tiddlers and normalized them into discrete nodes. -
Relationship Inference
It generated directional edges based on linguistic and semantic cues in the text. These edges were labeled with descriptive phrases derived from the underlying content. -
Structural Flattening
It produced a graph representation that made implicit assumptions visible. Concepts that felt intuitively connected were now linked explicitly.
What Ace did not do was evaluate coherence. It did not ask whether the abstraction levels were aligned, whether certain nodes were redundant, or whether a central thesis existed. It generated a plausible structure.
Next, I imported the structure into CmapTools (my favorite concept mapping software) using a simple tab-delimited text file (other import formats failed). Here again, AI did then necessary format conversions. The import preserved linking phrases and allowed manual rearrangement. Eliminating crossing lines forced clarification of hierarchy and exposed abstraction mismatches. The node/concept definitions were manually added as info notes.
At that point, the real work began. I removed some nodes, questioned ambiguous terms such as “learnability,” and examined whether each concept genuinely contributed to the central thesis. Some terms migrated to side modules. “Aging Well,” for example, remains connected by a dotted line, signaling a future expansion into lifespan cognition rather than a core dependency. Not surprisingly, the map isn't entirely coherent because the original notes were collected over time without any concern around a central question or well-defined Learning Sprint. They only had one thing in common, they were related to "cognition".
This map is not intended as a comprehensive theory. It functions as an interrogation device. Each node can be a starting point for further reading and research as well as future elaborations of the map. The map evolves as a record of structured reflection rather than a finished system.
Going forward, this experiment could become a template. Each Learning Sprint may generate its own evolving concept map, versioned and periodically reassessed. The value lies in making the architecture of thinking visible and open to revision.
