OpenNeuralingo

A neuroscience-driven AI language learning app that adapts through continuous human behavior feedback.

Comment

Inspiration

Language apps often optimize for time-on-app rather than how humans truly learn. From a neuroscience perspective, learning is driven by attention, reward, spaced repetition, and the specific errors a learner makes over time. I wanted to build a system where the AI doesn’t just generate content, but instead uses human behavior as a feedback signal—so learning materials continuously improve through interaction.

OpenNeuralingo started from a simple question:
What if a language app treated each learner as an experiment-of-one, adapting in real time based on behavior?

What it does

OpenNeuralingo is a neuroscience- and human behavior-driven AI language learning app that turns real-world content into adaptive practice.

The current version is designed for learners who already have some background in the language. It is not yet optimized for complete beginners.

Core idea: the system tracks learner behavior (e.g., self-assessment, errors, response patterns, revisit frequency) and uses that data to generate better next-step content.

Over time, the app can:

  • Personalize practice based on skill level and vocabulary gaps
  • Convert video subtitles, web content, or Markdown-based materials into exercises (listening, reading, targeted review)
  • Adjust difficulty and review timing using behavioral signals (spaced repetition, adaptive feedback, self-evaluation)
  • Create a closed learning loop:
    learner behavior → AI inference → improved content → better learning outcomes

How we built it

Frontend: React 19, TypeScript, Vite 6, Tailwind CSS
Backend: Python (Flask)
Database: SQLite
AI: Google Gemini API
Video: YouTube IFrame API (react-youtube)

Architecture highlights

  • Clean separation between user interface, backend logic, and AI generation
  • Lightweight learning-event database tracking performance and interaction history
  • Prompt pipelines conditioned on behavioral summaries (mistakes, weak areas, review patterns), grounding AI output in real learner data

Challenges we ran into

  • Transforming behavior into learning signals: User interactions are noisy and unstructured. We designed simple but robust metrics for adaptation.
  • Controlling AI output quality: Generative models can drift in difficulty and structure, requiring careful prompt engineering and constraints.
  • Latency and user experience: AI calls were optimized through caching and scoped generation flows.
  • Building a real adaptive loop: The hardest part wasn’t content generation—it was making each new exercise measurably better than the last.

Accomplishments that we're proud of

  • Built a full adaptive learning pipeline: content → behavior → AI personalization
  • Integrated real-world media into structured learning workflows
  • Implemented continuous personalization instead of static lesson paths
  • Delivered a modern, deployable web architecture

What we learned

  • AI alone doesn’t teach — measurement and feedback drive learning
  • Behavior data is the core signal for personalization
  • Learning experience design matters as much as model capability
  • Structured prompts and schemas outperform raw generation

What's next for OpenNeuralingo

  • Advanced learner modeling: finer skill diagnosis across vocabulary, grammar, listening, and comprehension
  • Personalized spaced repetition: moving beyond SM-2 to behavior-based scheduling
  • Beginner support: building structured learning paths for users with zero language background
  • More modalities: speaking/pronunciation feedback and richer listening tasks
  • Desktop version: packaging with Tauri/Electron for smoother media workflows
Share this project:

Updates