This AI Can Build Entire Fictional Languages From Scratch

Creating a fictional language has traditionally been a painstaking process requiring years of linguistic knowledge, careful planning and countless hours of creative work. Now, researchers are exploring whether artificial intelligence can automate much of that process.

A new research system called ConlangCrafter can generate fictional languages from the ground up, including their sounds, grammar, vocabulary and translation rules. The system has already been used to create more than 60 experimental languages, offering a glimpse into how AI could transform language design for games, films, books and linguistic research.

The research was presented at ACL 2026 by researchers associated with Tel Aviv University, Carnegie Mellon University and the University of California, Berkeley.

Unlike a conventional AI prompt that simply asks a large language model to “invent a language,” ConlangCrafter uses a structured multi-stage process designed to make the resulting languages more internally consistent.

From Tolkien’s Elvish to AI-Generated Languages

Creating a believable fictional language is nothing new.

J.R.R. Tolkien famously spent decades developing languages such as Quenya and Sindarin for his fictional worlds. His work involved detailed linguistic structures rather than simply replacing English words with invented ones.

More recently, linguist David J. Peterson developed Dothraki and other fictional languages for television and entertainment projects.

These examples demonstrate how much expertise can be required to create a convincing constructed language, commonly known as a conlang.

ConlangCrafter takes a different approach. Instead of relying entirely on a human linguist, the system uses multiple stages of AI generation and self-correction to develop the basic structure of a language automatically.

The researchers describe the system as a way to handle much of the structural work involved in language creation while leaving room for humans to refine the final result.

How ConlangCrafter Creates a Language

The system does not generate an entire language in one giant prompt.

Instead, ConlangCrafter divides the process into several connected stages.

The first stage focuses on phonology, determining which sounds exist in the language and how those sounds can be combined.

The second stage develops grammar and morphosyntax. This can include word order, grammatical categories, verb agreement, case systems and other structural rules.

The system then generates a vocabulary based on the language’s established rules.

Finally, it attempts to translate sample sentences into the newly created language. This provides an opportunity to test whether the rules created during earlier stages actually work together.

This sequential approach is important because language is highly interconnected. Changing one grammatical rule can create inconsistencies elsewhere.

ConlangCrafter therefore includes a self-refinement process designed to identify and correct contradictions before the system moves to the next stage.

Why a Single AI Prompt Is Not Enough

Large language models can already produce convincing-looking fictional words and grammatical descriptions.

However, asking an AI chatbot to create an entire language in one prompt can result in inconsistencies.

A model might establish one grammatical rule early in its response and then accidentally violate that rule later. Vocabulary may not follow the phonological rules, while sentence translations may contradict the grammar.

ConlangCrafter attempts to solve this problem through its staged architecture.

According to the researchers, the complete pipeline produces substantially better results than a simple single-prompt approach.

The system reportedly achieved diversity scores between approximately 0.56 and 0.60, compared with around 0.25 to 0.35 for the single-prompt baseline.

For comparison, a reference set based on thousands of real-world languages produces a score around 0.43 under the study’s methodology.

The researchers also reported that the full system was roughly 70% more consistent than the baseline approach.

Researchers Put the AI Languages to the Test

The researchers did not rely solely on automated measurements.

Two PhD-level linguists spent approximately 35 hours evaluating the generated languages and the system’s results.

Their assessments showed statistically significant relationships between the human evaluations and the consistency measurements used in the research.

That human evaluation is important because creating a language is not simply a matter of generating unusual words.

A useful fictional language needs rules that interact logically. Its sounds, grammar and vocabulary should form a system rather than a collection of unrelated inventions.

The evaluation suggests that structured AI pipelines can produce more coherent results than simply asking a language model to improvise.

AI Has Created Some Very Unusual Languages

More than 60 languages have reportedly been generated through the research project, and some of them demonstrate how far the system can explore unusual linguistic structures.

One particularly striking example is a consonant-free language.

The language uses only vowel sounds, creating a system that differs dramatically from the overwhelming majority of known human languages.

Another experiment explored a more unconventional communication system involving chromemes, representing color-based units, and kinemes, representing gestures.

These experiments demonstrate that AI-generated languages do not necessarily have to imitate familiar human languages.

Researchers can use the system to explore combinations of linguistic features that may be rare or absent in naturally occurring languages.

Potential Uses for Games, Films and Fantasy Worlds

The most obvious applications for ConlangCrafter are in creative industries.

Video game developers, fantasy authors, filmmakers and tabletop role-playing designers often need fictional languages to make imaginary cultures feel more realistic.

Creating a language manually can be expensive and time-consuming, particularly for smaller studios or independent creators.

An AI language-generation system could provide a starting point within a much shorter period.

A game developer could establish a fictional civilization and then use an AI tool to generate the basic linguistic framework associated with that culture. Human writers and linguists could subsequently modify the output to fit the project’s story and creative goals.

The technology could therefore function less like a replacement for professional linguists and more like a language-design assistant.

AI Could Also Help Linguistic Research

Entertainment is not the only potential application.

Researchers studying linguistics could use automatically generated languages to explore theoretical questions.

For example, researchers could generate artificial languages featuring unusual combinations of grammatical characteristics and examine whether those systems remain internally functional.

This could provide a faster way to create controlled linguistic experiments.

Instead of spending weeks or months manually constructing examples, researchers could potentially use an AI pipeline to generate multiple variations and then evaluate them.

Such experiments could help researchers investigate why particular grammatical structures are common across human languages and why others appear to be rare.

ConlangCrafter Still Has Important Limitations

Despite its impressive capabilities, ConlangCrafter is far from creating a complete human-quality language.

The current system focuses primarily on phonology, morphosyntax and an initial vocabulary.

It does not deeply model areas such as semantics, pragmatics, discourse or the complex contextual rules that influence how humans communicate.

The writing systems produced by the system are also not equivalent to the comprehensive scripts and orthographic traditions associated with real languages.

Another limitation comes from the underlying AI models themselves.

Large language models are trained disproportionately on widely spoken and well-documented languages. As a result, their outputs can naturally gravitate toward familiar linguistic patterns.

Even when researchers encourage greater diversity, AI-generated languages may still unintentionally resemble English or other major languages.

The researchers also found that some improvements, including certain consistency results for Gemini 2.5 Flash, did not reach statistical significance at the study’s sample size.

Could AI Make Fictional Languages Too Similar?

There is another interesting problem.

If AI-powered language-generation tools become widely available, creators could begin using similar systems to build fictional languages.

That could result in a new kind of creative homogenization.

Instead of every fantasy world developing a completely distinctive linguistic identity, AI-generated languages might begin sharing common structural characteristics because the underlying models were trained on similar data.

This could become particularly noticeable if entertainment companies rely heavily on automated world-building tools without substantial human refinement.

The technology may therefore make language creation easier while simultaneously making human creative direction more important.

A Reminder About Endangered Real-World Languages

The researchers also point to an important ethical consideration.

While AI can generate fictional languages in seconds or minutes, hundreds of real-world languages remain endangered and require documentation and preservation.

The resources being invested in artificial language generation should not distract from efforts to document languages spoken by vulnerable communities.

In that sense, ConlangCrafter highlights an interesting contrast: AI can now invent languages that have never existed while many naturally developed languages face the risk of disappearing.

The Future of AI-Powered Language Creation

ConlangCrafter represents an emerging category of AI systems that go beyond generating isolated pieces of content.

Rather than producing a paragraph or a list of words, the system builds an interconnected structure where different components influence one another.

That approach could eventually be applied to other creative and technical domains.

For world-building, AI could potentially generate not only a fictional language but also the culture, history, naming conventions and social structures associated with the people who speak it.

For researchers, similar systems could become tools for generating controlled experimental environments.

For writers and game developers, they could dramatically reduce the amount of time needed to build convincing fictional worlds.

But the technology is unlikely to eliminate the need for human creativity.

The most compelling fictional languages are often connected to a culture, history and worldview. Those elements require creative decisions that go far beyond grammar and vocabulary.

ConlangCrafter may therefore be best understood as a powerful starting point rather than an automatic replacement for linguists and world-builders.

As AI language-generation technology continues to improve, creating a fictional language could eventually become as accessible as choosing a font or designing a logo. The challenge will be ensuring that easier creation does not lead to less originality.

The tools may be getting better at building languages from scratch. What those languages ultimately say about their fictional worlds will still depend on the humans behind them.