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README.md

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-license: mit
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+---
+license: mit
+---
+
+---
+pipeline_tag: automatic-speech-recognition
+tags:
+- offline
+- edge-device
+- gradio
+- education
+---
+
+# 🧠 Edge-Optimized Math Tutor Orchestrator
+
+## Model Description
+This repository contains the orchestration and adaptive logic layer for an offline, CPU-bound AI Math Tutor. Rather than acting as a standalone monolithic weight file, this pipeline links extreme-edge open-source models with Bayesian logic to meet a strict < 75MB operating footprint constraint.
+
+### Core Architecture
+1. **ASR Component:** `openai/whisper-tiny` (39M parameters). Handles primary transcription and number extraction.
+2. **Adaptive Brain:** A bespoke Bayesian Knowledge Tracing (BKT) engine. Evaluates real-time P(mastery) against 5 distinct mathematical sub-skills to dynamically filter question difficulty.
+3. **Visual Grounding:** Procedural geometry generation (`Pillow`) entirely replacing traditional object-detection (e.g., OwlViT) to maintain the disk footprint and guarantee < 2.5s inference latency.
+
+### Code-Switching Capability
+The orchestrator includes custom logic layered over the ASR to detect and gracefully handle language code-switching (e.g., a child being prompted in Kinyarwanda but answering in English). 
+
+## Evaluation
+Tested against an Elo-style baseline on a held-out replay of 100 historical simulated student interactions. 
+* **BKT Model AUC:** Consistently outperforms standard Elo algorithms in predicting next-response correctness due to faster adaptation to early-learner "slip" and "guess" parameters. *(See `kt_eval.py` in the linked GitHub repo for exact metrics).*
+
+## Limitations
+Due to the strict 75 MB footprint constraint, this pipeline does not utilize quantized LLM adapters (QLoRA) for language generation, relying instead on high-quality, pre-generated localized stems and rule-based regex extraction.