PROPOSED AI APPLICATION

Research literature summary assistant

Researchers spend time summarizing many papers

Colleges and universitiesKnowledge and workflow pilotProposed solution scope

The problem and the proposed outcome

Application: Colleges and universities — Research literature summary assistant
Problem to solve: Researchers spend time summarizing many papers
Target buyer: Institution administrators, department heads
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether approved drafts require less preparation effort at the required factual standard.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Language drafting and summarization
Method code: L
AI contribution: Use a language model to organize permitted facts into a requested draft or summary; link facts to inputs and flag unknown details.
Output artifact: Editable draft or summary grounded in supplied facts.
Core modules: Approved facts / brief → draft generation → factual checks → human approval
Simple baseline: Reusable text templates, manual writing and rule-based mail merge.
Why test AI: Could help when language and structure vary between cases; template filling remains a useful lower-complexity baseline.

Pilot track: Knowledge and workflow pilot
Required data: Licensed papers; research queries; citations
Data readiness: Verified source facts, content rights, an approved template and examples of acceptable drafts are needed.
First development step: Gather permitted source-to-approved-draft examples and establish a factual review rubric.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Institution administrators, department heads: show a recent case where researchers spend time summarizing many papers, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Researcher-approved summary usefulness
Model evaluation: Assess factual consistency, completeness, unsupported statements, reviewer edits and approved preparation time.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: No special flag; domain review still applies
Human review boundary: Authorized user reviews critical facts, output quality and access before publishing, importing or acting.
Domain review: Use authorized learner data and approved resources; teachers review feedback and consequential educational decisions, with guardian authorization where needed.
Related micro-ERP catalogue IDs: IWA-B-4E3C026F441C; IWA-B-4143B957084E; IWA-B-E25A778522EE
Integration status: Potential segment link only; no product integration implemented.