MNee bypasses translation chains, connecting physical concepts directly to target language audio and text. Build instinctive neural connections through real-world affordances.
Each feature is grounded in established pedagogical research
Instantly connects physical concepts to target language audio and text, bypassing native translation chains. Concept ↔ Target Word
Vocabulary merges with real-world functional interactions. Language becomes an actionable environment affordance.
Per-skill mastery probabilities using Hidden Markov Models. 25+ micro-skills tracked across tones, phonetics, and grammar.
Dynamic Instance Hardness Controlled Learning selects items from your Zone of Proximal Development.
WebRTC-based speech-to-speech loop with Voice Activity Detection, barge-in support, and tonal audio synthesis.
Point your camera at objects in your environment. COCO-SSD object detection translates the world around you.
Track your cognitive mapping strength, skill mastery heatmaps, and affordance interaction density over time.
Semantic subgraph retrieval enriches every LLM interaction with linguistically accurate context.
Three steps from environment to acquisition
Camera or voice captures a real-world object or phrase in your environment.
Knowledge Graph + LLM enriches the detection with target language, grammar, and scaffolding.
BKT engine tracks mastery. ZPD scheduler picks your next optimal learning item.
Grounded in neuro-ecological instruction methodologies
By instantly connecting physical concepts directly to target language audio and text overlays, the platform bypasses native translation chains. The user builds strong, instinctive connections, eliminating native-language mental interference. Tracked via direct_method_strength scores per cognitive mapping.
Merging vocabulary with real-world functional interactions aligns perfectly with modern neuro-ecological instruction methodologies. Language ceases to be an abstract grammar system and transforms into actionable environment affordances. Tracked via environment_affordances table with interaction counts and context.
Delivering contextually relevant input naturally inside the user's daily environment builds a judgment-free language laboratory. This lowers communication and performative anxiety, driving high-fidelity subconscious retention. Tracked via affective_filter_score on user profiles and session-level deltas.