Mapping QuickCart's hybrid recommendation model to Indian schools and colleges. In EduPath AI, the commerce product becomes a learning opportunity, the student's current academic pathway becomes the basket, and the system recommends the next best learning action.
Product Promise
"The right learning opportunity for each student, at the right moment, with a clear reason and an accountable human in control."
A placement-and-skills recommender for Indian colleges — connecting student skills and approved academic records to verified courses, projects and job-role pathways. The school edition follows after age-verification and child-safety controls are operationally proven.
Buyer
Principal, dean, placement head, CIO
Pilot unit
1 department, 300–1,500 students
The system recommends the next best learning action, handles new-opportunity cold start, proposes alternatives when an option is unavailable, and explains every recommendation — mirroring QuickCart's four-branch recommendation architecture, retargeted for education outcomes instead of transaction volume.
The mapping preserves the recommendation problem while changing the domain vocabulary and success measure. Education optimises student progress and opportunity quality, not transaction volume.
| QuickCart | Education | Meaning |
|---|---|---|
| Customer | Student | Person receiving recommendations |
| Product | Learning opportunity | Course, certification, project, internship, assessment or resource |
| Product catalogue | Opportunity catalogue | Approved academic and skills inventory |
| Shopping basket | Current pathway | Current subjects, saved options, skills and stated goals |
| Purchase history | Learning history | Enrolments, completions and authorised activity |
| Products bought together | Opportunities completed together | Common sequence or co-learning pattern |
| New product | New learning opportunity | Offering with no historical participation |
| Out of stock | Unavailable opportunity | Full, cancelled, incompatible, expired or not offered |
| Substitute | Alternative opportunity | Available option satisfying comparable learning intent |
| Brand/pack size | Learning attributes | Subject, level, outcomes, prerequisites, credits and format |
| Basket value | Learner progress | Relevant action, completion, skill gain and adviser efficiency |
| Friendly explanation | Actionable rationale | Why it fits, what it requires and what differs |
Retained from QuickCart's architecture. Each branch solves one problem and produces structured outputs consumed by the next.
Branch 1 · GenAI
Extracts skills, outcomes, level, prerequisites and career relevance from approved catalogue documents. Catalogue owners review low-confidence fields.
Branch 2 · Classical AI
Item-item filtering learns institution-level co-learning patterns. Verified completion outweighs a view; dismissal is negative evidence.
Branch 3 · Hybrid
Borrows valid pathway associations from semantically related opportunities, applies every eligibility rule, then uses controlled exploration.
Branch 4 · GenAI-assisted rules
Deterministic filters run before scoring; explanations use verified facts and reason codes, never model speculation.
Student
Faculty Adviser
Placement Officer
Institution Administrator