PROPOSED AI APPLICATION

Personal learning path assistant

Learners struggle to choose next practice

Independent educators and mentorsHistorical data model pilotProposed solution scope

The problem and the proposed outcome

Application: Independent educators and mentors — Personal learning path assistant
Problem to solve: Learners struggle to choose next practice
Target buyer: Educators, mentors, course creators
Intended use: Personal / individual own use
Benefit to test: Test whether users find relevant approved options with less search effort.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Semantic recommendation and candidate ranking
Method code: M
AI contribution: Use embeddings, learned ranking or feedback models to shortlist approved options against explicitly stated requirements; show reasons and let users choose.
Output artifact: Evidence-backed shortlist with explanation and user feedback.
Core modules: Approved options / preferences → semantic ranking → reasoned shortlist → user selection
Simple baseline: Catalogue filters, keyword matching and manual shortlisting.
Why test AI: Could improve relevance when requirements are semantic or feedback-rich; simple filters may be sufficient for small catalogues.

Pilot track: Historical data model pilot
Required data: User goals; owned work samples; approved resources
Data readiness: An approved option catalogue, lawful stated preferences or requirements, representative relevance judgements and feedback are needed.
First development step: Define permitted options and relevance criteria, collect reviewed query-to-option examples and compare catalogue filters.
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 Educators, mentors, course creators: show a recent case where learners struggle to choose next practice, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: User-rated next-task relevance
Model evaluation: Measure precision or ranking relevance at the shortlist size, user-rated usefulness, coverage and acceptance; inspect consequential errors.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: No special flag; domain review still applies
Human review boundary: Reviewer or user confirms labels, exceptions, links or shortlist relevance; no automatic adverse or consequential decision.
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-D1D534A36C0D; IWA-B-548B70921E31; IWA-B-AD21148E3C29
Integration status: Potential segment link only; no product integration implemented.