Recently, I’ve been thinking a lot about how computational linguistics and AI intersect with real-world issues, beyond just building better chatbots or translation apps. One question that keeps coming up for me is: Can AI actually help save endangered languages?
As someone who loves learning languages and thinking about how they shape culture and identity, I find this topic both inspiring and urgent.
The Crisis of Language Extinction
Right now, linguists estimate that out of the 7,000+ languages spoken worldwide, nearly half are at risk of extinction within this century. This isn’t just about losing words. When a language disappears, so does a community’s unique way of seeing the world, its oral traditions, its science, and its cultural knowledge.
For example, many Indigenous languages encode ecological wisdom, medicinal knowledge, and cultural philosophies that aren’t easily translated into global languages like English or Mandarin.
How Can Computational Linguistics Help?
Here are a few ways I’ve learned that AI and computational linguistics are being used to preserve and revitalize endangered languages:
1. Building Digital Archives
One of the first steps in saving a language is documenting it. AI models can:
- Transcribe and archive spoken recordings automatically, which used to take linguists years to do manually
- Align audio with text to create learning materials
- Help create dictionaries and grammatical databases that preserve the language’s structure for future generations
Projects like ELAR (Endangered Languages Archive) work on this in partnership with local communities.
2. Developing Machine Translation Tools
Although data scarcity makes it hard to build translation systems for endangered languages, researchers are working on:
- Transfer learning, where AI models trained on high-resource languages are adapted to low-resource ones
- Multilingual language models, which can translate between many languages and improve with even small datasets
- Community-centered translation apps, which let speakers record, share, and learn their language interactively
For example, Google’s AI team and university researchers are exploring translation models for Indigenous languages like Quechua, which has millions of speakers but limited online resources.
3. Revitalization Through Language Learning Apps
Some communities are partnering with tech developers to create mobile apps for language learning tailored to their heritage language. AI can help:
- Personalize vocabulary learning
- Generate example sentences
- Provide speech recognition feedback for pronunciation practice
Apps like Duolingo’s Hawaiian and Navajo courses are small steps in this direction. Ideally, more tools would be built directly with native speakers to ensure accuracy and cultural respect.
Challenges That Remain
While all this sounds promising, there are real challenges:
- Data scarcity. Many endangered languages have very limited recorded data, making it hard to train accurate models
- Ethical concerns. Who owns the data? Are communities involved in how their language is digitized and shared?
- Technical hurdles. Language structures vary widely, and many NLP models are still biased towards Indo-European languages
Why This Matters to Me
As a high school student exploring computational linguistics, I’m passionate about language diversity. Languages aren’t just tools for communication. They are stories, worldviews, and cultural treasures.
Seeing AI and computational linguistics used to preserve rather than replace human language reminds me that technology is most powerful when it supports people and cultures, not just when it automates tasks.
I hope to work on projects like this someday, using NLP to build tools that empower communities to keep their languages alive for future generations.
Final Thoughts
So, can AI save endangered languages? Maybe not alone. But combined with community efforts, linguists, and ethical frameworks, AI can be a powerful ally in documenting, preserving, and revitalizing the world’s linguistic heritage.
If you’re interested in learning more, check out projects like ELAR (Endangered Languages Archive) or the Living Tongues Institute. Let me know if you want me to write another post diving into how multilingual language models actually work.
— Andrew
CompLing Insights 
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