Depending on who you talk to, AI is either about to transform language learning or it doesn’t help at all. One of these groups says these tools are a shortcut that will hollow out your language. The other says a chatbot is a tutor in your pocket and the only remaining question is which one to subscribe to. Both of these approaches oversimplify the situation.

The evidence supports a narrower conclusion. Chatbots can create useful practice and feedback, but they can also shortcut the retrieval and production the learner needed to do to make progress.

The evidence is stronger than the skeptics expect

Three meta-analyses published in 2025 looked at this question independently and landed in close agreement. One of these was Wang, Cheung, Neitzel and Chai, who analyzed 70 effect sizes and found chatbot users gained more than non-users, with the largest gains among adult and secondary-school learners rather than children.

Effects in that range are meaningful, but they do not tell us that every chatbot, feature or prompt works equally well. They support using AI as one practice tool, not treating it as a complete method.

One important finding was that there were larger effects for interventions lasting only one to seven days, which is the signature of a novelty effect.

What AI is genuinely good at

Across the studies, there are clear benefits: immediate responses, repeatable low-stakes practice, and availability at the hour you actually study.

The anxiety finding is especially relevant to learners who avoid speaking. Ding and Yusof assigned 60 exam-prep students to six weeks of practice with an AI conversation app or to conventional speaking practice, and the AI group showed significant gains in speaking scores alongside significant reductions in speaking anxiety. A machine lets you restart the same sentence repeatedly without social pressure. That can make it easier to attempt more language.

Chatbot practice may also work better outside a classroom than inside one, where time and turn-taking constraints limit practice. For a self-directed learner practicing at 6am, this is a huge advantage.

Where it costs you something

The clearest warning comes from general education rather than language learning.

A peer-reviewed 2025 field experiment gave nearly 1,000 high-school students access to GPT-4 while they practiced mathematics. The standard chatbot-style version raised practice scores, but on a later unaided exam those students scored roughly 17 percent worse than students given no tool at all. A version of the experiment that withheld answers and used teacher-designed hints removed that loss, although it did not outperform the no-tool group on the unaided exam.

The subject was mathematics, so the percentage should not be directly projected onto language learning. The design lesson is still relevant: assistance can raise performance while it is present without building the skill needed when the assistance disappears.

General-education researchers describe a performance paradox: AI can improve the work in front of you while weakening what you can later do alone, because the tool performed some of the retrieval or generation that would have built the skill. Cognitive offloading is not automatically harmful. It becomes a learning problem when you offload the exact ability you meant to practice.

Language learners feel this in real time. Ask a chatbot to write your message to a colleague in Spanish and the message will be better than yours. Your Spanish will not be.

“I am afraid I will get used to it too much and my English will deteriorate.”

A learner interviewed in an exploratory study of ChatGPT use for language tasks

That learner named the risk without prompting. Others in the same sample reported checking the tool’s output critically rather than accepting it. Dependence is not automatic, and the learner still controls where the tool enters the task.

Grammar explanations are the weakest link

There is one specific failure mode serious learners should know about: explanations that sound authoritative and are wrong.

A 2025 study tested 12 ChatGPT-based bots on grammatical queries. Accuracy ranged from 0 to 80 percent, and not one of the 12 scored 100 percent. Instructors in the same study reported students over-trusting chatbot grammar explanations and building misconceptions from them.

This matters more at the intermediate and advanced stages than at the beginner stage. A beginner asks about the present tense, and the tool answers reliably. You ask why the subjunctive appears after one verb of opinion but not another, or which past tense a specific Hebrew construction takes, and you have moved into the territory where a confident wrong answer is indistinguishable from a right one, because if you could tell the difference you would not have asked.

The practical protection is not to stop asking. It is to treat an explanation as a hypothesis until a second source confirms it, and to prefer sources with an actual authority behind them: the Real Academia Española for Spanish, the Académie française or the OQLF for French, the Academy of the Hebrew Language for Hebrew, or a curriculum a human being wrote and checked.

Produce before you ask

These language-learning studies suggest that an approach where you produce a structure first allows you to get benefits out of AI tools without the pitfalls.

Keep AI between attempts: produce the language yourself, use the tool to explain one error, then produce it again without help.

Keep AI between attempts: produce the language yourself, use the tool to explain one error, then produce it again without help.

Keep AI between attempts: produce the language yourself, use the tool to explain one error, then produce it again without help.

The evidence supports both halves without pretending they are the same study. The guarded math tutor withheld direct answers and avoided the loss seen with unrestricted AI. The language-learning study reduced anxiety while learners repeatedly attempted speech. In each case, the useful version kept the learner doing the target work.

What this looks like in a week of practice

Concretely, for a learner at the intermediate level:

Write the message before you check it. Draft the email, the reply, the paragraph. Then ask what is wrong with it and why. The first version being clumsy is you learning.

Ask for the rule, then ask for a second case. An explanation you can only apply to the sentence that produced it has not transferred. If the tool names a rule, test it immediately on a sentence it has not seen.

Use it to raise difficulty, not to remove it. Ask for a slightly more demanding version of your paragraph, then explain which changes you understand and which you do not. Or ask it to identify up to three error patterns, then verify the grammar claims. Both keep you working.

Speak first, transcribe second. The repetition tolerance is genuinely valuable. Say the thing badly several times before you ask how to say it well.

Verify grammar claims against a real authority. Especially for the constructions you are still getting wrong, where you cannot check the answer yourself.

Where a structured curriculum fits

A general-purpose chatbot answers the question you thought to ask. It may remember earlier exchanges, but it does not automatically provide a teacher-authored sequence or decide which gap should come next. That matters when the error you made three weeks ago is still shaping what you avoid today.

Dioma is built to combine the power of AI tools with the quality of a human-built curriculum. You produce the language first, in speaking and writing tasks drawn from a curriculum teachers wrote and reviewed. The correction responds to what you produced, connects the error to the relevant topics, and brings recurring patterns back into later practice. Across Spanish, French and Hebrew the curriculum contains about 750 topics.

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