Building a student assessment Evaluation platform for Educational Initiatives

Educational Initiatives (EI) captures student literacy and numeracy assessments on tablet applications in the field. To verify these assessments, especially audio-heavy tests that cannot be automatically graded on the device, we built a full-stack Evaluation portal. The platform allows human evaluators and automated systems to review, transcribe, and grade student submissions.

The core workflow

The scoring workflow processes data through three stages:

Assessment sync: field devices upload student responses to the sync API, which registers them in a pending evaluations queue.
Autoscoring: a batch process scores objective items (such as multiple-choice questions or mathematics processes) and creates the initial database records.
Manual and automated review: human evaluators grade remaining audio subtests (like oral reading fluency and vocabulary), while administrators use machine transcription and custom rules to verify accuracy.

Technical architecture

The platform split into two decoupled codebases:

Frontend (eval-portal): a React single-page application that provides a scoring interface for evaluators and administration controls for system managers.
Backend (reeval-portal-api-node): an Express API built with TypeScript and MongoDB, handling authentication, audio processing, WebSocket synchronization, and third-party transcription services.

Real-time scoring and optimistic updates

To keep the interface responsive, the portal handles writes through a WebSocket protocol (version 2.1). When an evaluator scores a subtest, the client pushes updates optimistically. The server resolves revision numbers and broadcasts the state across open tabs, preventing data loss and eliminating interface latency.

Speech processing and verification

For oral tests, we integrated the Sarvam speech-to-text API to transcribe student audio files. A validation service compares the transcript against the expected passage using fuzzy text matching. Evaluators review the matching metrics alongside wave visualizations generated by Wavesurfer, flagging noisy or incomplete files.

Multi-version score management

Because multiple human and automated scorers can grade the same student response, the database supports versioned score records. Administrators can view comparison tables across all versions (such as machine, evaluator, or rescore runs) before selecting the final active score.

Actual screenshots and interface designs cannot be displayed due to non-disclosure agreements.

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Replaying mathematics process telemetry

The Grid Replay System reconstructs student arithmetic attempts keystroke-by-keystroke. By converting field telemetry into visual models, the platform allows researchers to analyze student problem-solving methods and identify areas of difficulty in mathematics education.

Arithmetic playback and grid interface

The application renders a nine-by-nine interactive cell grid representing the student workspace. The playback interface provides controls to play, pause, and step through calculations. As the replay runs, the active cell highlights to show digit input, carry operations, and backspaces alongside real-time timestamps.

Telemetry parsing and conversion

The system processes telemetry from two data paths:

Live assessments: parses active student data streamed from numeracy assessments.
Offline multiplication logs: converts legacy session telemetry, reconstructing missing timestamps and translating six-by-six grid structures into the nine-by-nine format.

Error detection and insights

The statistics dashboard verifies telemetry quality by comparing final student answers against values calculated by the replay engine, flagging mismatching records. A question insights page groups attempts by operand pairs (such as specific multiplication questions), ranking them by accuracy and speed to help researchers study common mistakes.

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Building a scoring quality control audit system

The Audit System is a quality control interface for student evaluations. It compares field scores against auditor and rescorer grades, tracking evaluator consistency and maintaining data hygiene across multi-round assessments.

Dual-track verification

The system operates across two separate verification tracks:

LSEP audit quality control: compares field-evaluator scores against independent audits. When discrepancies arise, a third rescorer evaluates the attempt to establish a final grade.
CSF multi-round study: tracks student progression across multiple reevaluation rounds, matching scores against task groups and experimental arms from local configurations.

Reliability metrics

The platform calculates inter-rater reliability scores for individual evaluators. It tracks the volume of audits, calculates the average score difference, and flags evaluators whose variance exceeds established margins (such as a ten percent threshold for oral reading fluency). High-disagreement rates trigger alerts, helping administrators identify training needs.

Data integration

The interface integrates with external databases and Amazon S3 storage. Users can filter audits by date, student, or completed evaluations, listen to student audio clips directly in the browser, and navigate to side-by-side comparison interfaces. A maintenance worker identifies incomplete student evaluations that have already been audited, ensuring database hygiene.

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Reasonable is a high-performance, reasoning-centered AI agent service built on the modern LlamaIndex Workflows architecture.

Unlike traditional LLM wrappers that simply "get an answer," Reasonable is designed with a "Reasoning-First" philosophy. Here’s a breakdown of what makes it unique:

Core Architecture

Workflows-Native: It uses the v0.14+ LlamaIndex event-driven architecture, which allows for complex, multi-step agentic behavior (like the ReAct loop) that is far more robust than legacy sequential agents.
Chain-of-Thought (CoT) Extraction: It is specifically engineered to handle "Thinking" tokens. It identifies and streams internal reasoning processes separately from final answers, providing total transparency into how the AI arrived at a conclusion.
Ollama Integration: It is locally-optimized, currently leveraging models like Qwen 3.5 (9B) to perform heavy reasoning tasks without relying on external cloud APIs for the "brain" of the operation.

The "Socratic" Philosophy

Following our latest architectural pivot, Reasonable has transitioned from a simple "solver" into a Socratic Tutor.

The Heuristic Ladder: Instead of blurting out answers, the agent uses diagnostic tools to analyze the "shape" of a problem, then guides the user through identification, strategy, and execution.
Scaffolded Interaction: It treats tools as "Math Diagnostic Engines" that provide facts to the agent, which then uses those facts to challenge and guide the user.

Technical Stack

Backend: FastAPI with full CORS support and SSE (Server-Sent Events) streaming.
Protocol: A specialized Event Schema (pulse, reasoning, call, result, message) that allows frontends to render complex agentic thoughts and tool interactions in real-time.

In short, Reasonable is about making AI interaction more transparent, educational, and - as the name implies - driven by clear, observable reasoning.

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Building a Culturally-Aware Name Generator

Language is not just a bag of letters; probabilities belong to a culture. This package generates names that accurately reflect specific regional phonology by carefully curating training data and employing constrained Markov chains.

The Data Challenge

Most name generators fail by mixing names from all over the world into a single pool, teaching the model incorrect letter patterns. To solve this, the generator relies on official datasets (like Ireland's CSO or Sweden's SCB) and enforces strict regional boundaries. Names are tagged by region, and overlapping names can be filtered using an exclusive pool to ensure the generated names maintain the correct cultural shape.

Procedural Generation

The package supports different generation strategies:

Markov Chains: Uses an order-3 letter model to analyze sequences in the curated datasets and sample new, unseen names that follow the exact same statistical rules.
Morphological Filtering: Restricts generation to names that fit specific stem-and-suffix rules.
Register: Directly samples from validated historical birth registers.

Performance and Architecture

Built for Node 20+, the package uses native node:fs for rapid JSON parsing and node:crypto for uniform random sampling. The published NPM package minifies the regional datasets, delivering a robust world-building tool in a highly optimized footprint.

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