Explanations · Graphics · Solutions · Code
Resources investedCompute · Energy · API fees · Human time
“A broad access to capable AI leads to decentralized innovation.”
Students already use commercial AI for coursework. An institutional service provides equitable access while keeping responsibility for data, usage rules and budgets with the university.
We need a data economy for AI-generated knowledge.
When someone develops a good explanation, creates a useful graphic with AI, or finds an interesting solution, work has gone into it. Computing resources were used, time was invested, and human decisions were made.
Yet much of this knowledge disappears into personal chat histories.
Prompt, Process, Provenance
Research Data Alliance · framework proposal
Define the question and context.
Generate, evaluate and revise.
Preserve the record for future work.
Low-barrier access to capable models, accessible interfaces, responsible-use teaching and transparent cohort budgets.
Permissionless innovation within agreed data and resource boundaries. Members can address institutional problems from within.
Transparent model choices, clear data handling and deliberate sharing. Private by default; no hidden prompt analysis or automated student rankings.
Preserve reviewed results with their context, version and permissions. Make them searchable and reusable without losing their origin.
Git keeps a history of saved changes; GitLab makes these files accessible as projects with access permissions. ChatBHT already archives conversations, metadata and agent files there. A commit link points to one exact saved version, so later changes do not obscure what was originally recorded.
GitLab System Hooks are automatic notifications from the archive server. When a project is created or new files are uploaded, an enabled hook can notify a processing service. The proposed service would check permissions and queue approved changes across projects, instead of checking every archive manually.
An extractor would read changed archives and turn the recorded details into a consistent entry: prompt, model, context, output and human contribution, where available. Each entry would retain its source version. Results from different projects could then be searched and compared in the same way.
interaction_idtimestampsubjectmodelmodel_versionpromptcontext_sourcesoutputtool_callsartefactstoken_usagevisibilitylicensehuman_reviewhuman_changescommit_idA separate search store would catalogue approved entries, avoiding the need to reopen thousands of projects for each question. It would support keyword searches, topic similarity and aggregate statistics. Changes to permissions or deleted source material would also have to be reflected in the catalogue.
A knowledge graph would connect entries through named relationships: which source was used, which dialogue produced a graphic, or where a result was reused. Similarity could suggest related work; human review would distinguish these suggestions from verified connections.
An AI-assisted search would answer questions using the institution's approved archives and link back to the recorded versions. It could reveal related work across departments and partner institutions. Personal introductions would be opt-in; access permissions would still apply to every result.
Which MQTT security approaches have already been developed at our university?
Which prompts produced useful explanatory graphics for mechatronics?