# DIY-MKG: An LLM-Based Polyglot Language Learning System

arXiv: https://arxiv.org/abs/2507.01872 · HTML: https://mlsanity.com/paper/2507.01872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [LLM-as-a-tutor in EFL Writing Education: Focusing on Evaluation of Student-LLM Interaction](https://mlsanity.com/paper/2310.05191/) (2023)
- [RECIPE: How to Integrate ChatGPT into EFL Writing Education](https://mlsanity.com/paper/2305.11583/) (2023)
- [Creative and Context-Aware Translation of East Asian Idioms with GPT-4](https://mlsanity.com/paper/2410.00988/) (2024)
- [RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education](https://mlsanity.com/paper/2403.08272/) (2024)
- [Adaptive Forgetting Curves for Spaced Repetition Language Learning](https://mlsanity.com/paper/2004.11327/) (2020)

## Abstract (arXiv)

Existing language learning tools, even those powered by Large Language Models (LLMs), often lack support for polyglot learners to build linguistic connections across vocabularies in multiple languages, provide limited customization for individual learning paces or needs, and suffer from detrimental cognitive offloading. To address these limitations, we design Do-It-Yourself Multilingual Knowledge Graph (DIY-MKG), an open-source system that supports polyglot language learning. DIY-MKG allows the user to build personalized vocabulary knowledge graphs, which are constructed by selective expansion with related words suggested by an LLM. The system further enhances learning through rich annotation capabilities and an adaptive review module that leverages LLMs for dynamic, personalized quiz generation. In addition, DIY-MKG allows users to flag incorrect quiz questions, simultaneously increasing user engagement and providing a feedback loop for prompt refinement. Our evaluation of LLM-based components in DIY-MKG shows that vocabulary expansion is reliable and fair across multiple languages, and that the generated quizzes are highly accurate, validating the robustness of DIY-MKG.