Academic
Intelligence Engine.
An AI-assisted research platform designed to preserve, organize, and extend the intellectual frameworks of academic researchers.
A scholar's intellectual framework lives mostly in their head.
A researcher accumulates a body of work over decades — published papers, working drafts, lecture notes, citation networks, and the unwritten reasoning that connects them. Most of this structure remains tacit. Students inherit only the published surface; collaborators inherit only what is reachable through search.
The Academic Intelligence Engine asks a narrow question: can a system preserve the connective tissue between a researcher's ideas in a form that supports new thinking, rather than merely recall?
The goal is not to replicate a scholar. It is to make their framework navigable.
A typed knowledge graph anchored in the researcher's own corpus.
The corpus is decomposed into typed nodes — works, concepts, claims, methods, and citations — connected by typed edges that record how the researcher themselves links them. Provenance is preserved at every node so that every retrieved fragment is traceable to a primary source.
Semantic retrieval grounded in the graph, not the model.
Queries are answered through retrieval-augmented generation over the corpus. The model proposes; the graph constrains. Generation is never allowed to assert a claim that is not anchored to a retrievable source within the researcher's own body of work.
An assistant in the loop, not at the wheel.
The researcher asks questions, drafts arguments, and traces concept lineages. The engine responds with grounded passages, suggested connections, and citation suggestions — each presented with a clear path back to the source the researcher already wrote.
A framework students can interrogate, not just memorize.
In a teaching context, the engine functions as a navigable map of the course's underlying intellectual structure. Students can ask why two ideas connect, see the citation chain that supports a claim, and locate the primary readings that originated it.
Toward a long-lived intellectual instrument.
The roadmap explores cross-corpus reasoning across collaborating researchers, version-aware tracking of how a concept evolves over a scholar's career, and an annotation surface that captures the researcher's reactions as first-class graph edges.
Augment scholarship. Do not impersonate it.
The engine is deliberately conservative about generation. It cites more than it asserts, surfaces the researcher's own words wherever possible, and treats every inference as a hypothesis the scholar must accept before it joins the graph.
The instrument should sharpen the scholar, not stand in for them.
Provenance is the product.
A claim without a source is worse than no claim at all.
The scholar is the ground truth.
Every model output is a draft awaiting endorsement.
Frameworks outlive papers.
Preserve the connective tissue, not just the published surface.
Every system in the Decision Systems Lab is engineered to outlive its author : a small, durable piece of operational thinking made legible to the next engineer.
Technical documentation for this system is currently being developed as part of the Decision Systems Lab. This page will evolve alongside the project as research, implementation, and validation progress.