Academic Matching Prototype
Explainable guidance for course selection
Built a proof of concept that maps profiles, skills, and learning goals to course options using semantic scoring and transparent, taxonomy-based alignment.
The Challenge & Goal
The mismatch problem
Students often struggle to find courses aligned with their actual competencies, career goals, or skill progression. Traditional matching systems rely on static keyword mapping or manual advisor input - neither scalable nor intelligent.
What the client needed
They wanted a matching engine that could analyze profiles and learning objectives, then suggest courses based on cognitive depth alignment (taxonomy scoring). The system needed to make nuanced distinctions - like differentiating between "understand" and "apply" level outcomes - while staying fast and transparent.
Our mission
We set out to build a fast, explainable prototype capable of mapping thousands of profiles to course modules in near real time. The focus was not just accuracy, but interpretability, scalability, and measurable alignment.
Our Approach
What we did
- Built a parsing pipeline for profiles and course outcomes.
- Implemented cognitive-level tagging to score fit with transparent reasoning.
- Added a retrieval layer for semantic matching at scale.
- Designed a clean UI for interactive queries and recommendations.
- Deployed a test environment with benchmarking for latency and accuracy.
Our process
Reviewed course data and sample profiles. Defined evaluation metrics and created a taxonomy scoring approach for alignment.
Implemented semantic retrieval and a scoring layer. Connected frontend and backend APIs for interactive recommendations.
Deployed the POC to a test environment. Benchmarked latency, quality, and scalability and shared results with stakeholders.
Services covered
Architecture & Stack
- Responsive UI
- Fast search experience
- Clear result explanations
- Recommendation endpoints
- Scoring + explanations
- Validation
- Structured records
- Vector retrieval store
- Caching layer
- Semantic embeddings
- Taxonomy alignment
- Reasoned explanations
Deployment pipeline
Stack summary
- Search UI — Fast, clear interactions
- Results — Transparent scoring and reasoning
- UX — Mobile-first layout and accessibility
- APIs — Typed responses and validation
- Retrieval — Vector-based similarity search
- Caching — Lower latency on repeat queries
- Embeddings — Semantic representations
- Alignment — Taxonomy-based scoring
- Explanations — Traceable reasoning output
Key integrations
Results & Impact
Delivered a working course matching POC with real-time recommendations and clear, explainable scoring.
Demonstrated measurable improvements in relevance and usability for guidance workflows during testing.
- Profile and course outcome parsing
- Semantic similarity scoring
- Taxonomy-based alignment visualization
- Interactive recommendation UI
- Transparent explanations for each result
- Evaluation-ready outputs
Innovations
Enabled cognitive-depth based matching instead of keyword similarity.
Optimized semantic search delivering sub-second results.
Each recommendation included a traceable breakdown of why it was suggested.
- Improved recommendation relevance in evaluation runs.
- Enabled scalable guidance without manual keyword tuning.
- Created a foundation for larger datasets and future deployments.
- Planned user validation with academic stakeholders to refine scoring logic.
- Roadmap to expand datasets and improve ranking with broader signals.
- Containerization planned for multi-tenant deployment options.
- Benchmarking planned on larger datasets for the next iteration.