AI in Language Learning 

The rise of generative AI tools is changing the landscape of language learning. While these tools offer exciting opportunities for language pedagogy, they also come with potential pitfalls and drawbacks. To incorporate AI into your language classroom, it helps to have a basic understanding of how generative AI works and what it can and cannot do.

How does generative AI learn languages?

Every piece of human writing carries a unique and distinguishable voice. Even when we write in the same language and follow its grammatical rules and structures, we make our own aesthetic choices, reflected through the nuances of applied language. What distinguishes today's AI from earlier computer technologies is that it is quite good not only at understanding the subtleties of human language but also at reproducing it at or near the human level.

How does AI do this? Generative AI is built on Large Language Models (LLMs). In simple terms, an LLM "reads" a very, very large amount of text to identify "patterns," which it then uses to "predict" the next word in a sequence. From writing computer code to translating languages, the model is performing the same core function it was trained to do (really well!): predicting what comes next.

How is AI so good at mimicking human language?

It has been fed an enormous amount of data. The original GPT-3 model, for instance, was trained on roughly 570 GB of filtered text—on the order of 300 billion "tokens" (the sub-word units models actually count, which run a little fewer than words). For context, the complete Harry Potter series (seven books) contains just over a million words, so you can think of GPT-3 as having absorbed the equivalent of hundreds of thousands of those series.

It's worth noting that GPT-3 is now an early-generation model. The systems most students use today—GPT-4 and GPT-5–class models, Anthropic's Claude, and Google's Gemini—are trained on substantially larger and often multimodal datasets (text, images, audio, and more). Unlike GPT-3, the exact training-data sizes for these newer flagship models are generally not disclosed by their developers. The basic principle, though, is unchanged: scale plus pattern-prediction is what lets these tools mimic language so convincingly.

AI in the language classroom

Given the immense data these models absorb, an LLM can mimic language in ways that would be impossible for any single person. You can ask it to write a poem in Chaucer's style or Rupi Kaur's, to draft a speech in the cadence of a particular public figure, or to talk like a slang-heavy young adult texting or a business executive presenting in a boardroom. For language learners, this opens up nearly infinite possibilities to practice using language in authentic contexts.

Here are some tangible ways you can incorporate AI in your language classroom:

• Set up roleplays. Ask students to roleplay with an AI tool in a particular setting—say, a marketplace in Cairo. Students can submit copies of their conversations as assignments.

• Practice speaking out loud. This is the biggest change since these tools first appeared: many now support real-time voice conversation, not just text. ChatGPT's basic voice mode is free (with a more advanced voice mode on the paid tier), and tools like Speak and Duolingo's AI "Video Call" feature let learners hold spontaneous spoken conversations and get feedback. This turns the roleplay idea above into genuine speaking practice that was once available only to those who could travel or hire a tutor.

• Get pronunciation feedback. Tools such as Elsa Speak and Speechling—and the voice features above—can analyze pronunciation and suggest corrections.

• Build vocabulary. AI tools can generate personalized vocabulary lists suited to a learner's proficiency level, or customized lists for particular topics and contexts.

• Prepare for exams and certification. AI can provide sample responses at different proficiency levels and offer feedback on student answers (treat any "scores" as rough guidance, not official assessment).

• Explore translation and paraphrasing. AI can offer translations in multiple styles so students can observe the nuances of moving between languages, and can paraphrase to show how the same idea can be expressed in different ways depending on tone and situation.

• Request corrections and feedback. Students can ask for corrections on their work along with explanations, giving them a low-stakes way to experiment with their language abilities and get quick feedback outside of class.

💡 Tip from HumTech. Check out HumTech's "AI and Language Learning: Practice Conversation Skills" for more suggestions:

"Invite your students to take the chance to be creative with their AI practice conversations. Encourage them to be creative in making things up: Who are you today? What's your gender, age, and nationality for today's homework? Maybe for today's assignment, you're a Greek engineering grad student who loves sushi and knitting. And maybe tomorrow you are a 70-year-old Indonesian grandma who's getting her astronomy degree and likes K-pop." This not only protects the student's personal data, but also has the student test their proficiency across different grammatical forms.

What to watch out for

Less commonly used languages have a smaller digital footprint. Of the world's roughly 7,000 languages, only a small fraction have enough digital text for AI tools to handle well, and many oral-tradition, indigenous, and regional languages are barely represented. Even when a language is well represented overall, there's no guarantee that all of its dialects are—so relying exclusively on AI can mean that dominant dialects get the most attention, further marginalizing regional and less commonly used varieties.

Bias can seep in. Because AI is trained on existing human content, biases—including but not limited to gender, political, and cultural biases—can surface in its output. Human oversight matters to ensure AI-generated content doesn't reproduce these biases.

Grammar explanations can be wrong. AI tools have been known to give inaccurate grammatical explanations, especially for more advanced or complex structures. Newer models are more fluent, but they still hallucinate, so this caution stands: don't rely on AI for grammar instruction—use trusted, verified sources instead, and have students confirm anything important.

Mind data privacy. Students should understand that information shared with an AI tool can be stored and used to train future models. Encourage them to avoid sharing personal or identifying information—the creative-persona approach in the HumTech tip above is one good workaround.

Beware over-reliance. Leaning too heavily on AI can mean students don't get enough practice using the language in real social settings. Frame AI as a supplement to language learning, not a replacement for the irreplaceable work of real-life interaction.

Further reading

• • Li, M., Wang, Y., & Yang, X. (2025). Can generative AI chatbots promote second language acquisition? A meta-analysis. Journal of Computer Assisted Learning, 41(4), e70060. https://doi.org/10.1111/jcal.70060

  • Li, Y., Zhou, X., & Chiu, T. K. F. (2025). Systematic review on artificial intelligence chatbots and ChatGPT for language learning and research from self-determination theory (SDT): What are the roles of teachers? Interactive Learning Environments, 33(3), 1850–1864. https://doi.org/10.1080/10494820.2024.2400090

  • Dai, D. W., Suzuki, S., & Chen, G. (2025). Generative AI for professional communication training in intercultural contexts: Where are we now and where are we heading? Applied Linguistics Review, 16(2), 763–774. https://doi.org/10.1515/applirev-2024-0184