AI Speaking Partners for English Practice: Why Task Design Matters
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AI Speaking Practice for English Learners: Why Task Design Matters
Many English learners say the same thing:
“I understand English, but I can't speak it.”
Part of the problem is straightforward. Developing speaking requires opportunities to speak, yet classroom time is limited, classes can be crowded, and fear of making mistakes may prevent learners from participating.
AI speaking partners offer an interesting opportunity. Learners can practise whenever they want, repeat the same interaction, experiment with different expressions and rehearse without immediately facing the pressure of speaking in front of a class.
But there is an important distinction:
Speaking more with AI does not automatically mean learning to speak better.
The crucial question is not which AI tool we use, but what the learner is actually doing with it — and whether that experience transfers to communication with real people.
What does the research tell us?
A 2026 study by Huang and Kakham published in Frontiers in Education offers some useful evidence.
The study involved 83 Chinese university students. Learners in the AI-supported group performed significantly better than the control group in overall oral proficiency.
The findings on speaking anxiety, however, are particularly interesting.
There was no statistically significant difference between the groups in overall foreign-language speaking anxiety after the intervention. When researchers examined individual task types, however, situational anxiety was lower for the AI group in pair work and presentations, but not in debate or storytelling.
That distinction matters.
The educational impact of AI depends partly on the task we design around it.
“Talk to AI” is not a speaking task
Telling students:
“Open a chatbot and speak English for ten minutes.”
may be a technology activity, but it is not necessarily a well-designed learning task.
Start with a communicative purpose.
Is the learner trying to:
make a hotel reservation?
disagree politely?
propose a solution?
tell a story?
ask for clarification?
Real speaking involves more than producing grammatically correct sentences.
Learners must interpret another person's intentions, respond appropriately, clarify misunderstandings, reformulate ideas and keep an interaction moving.
This is where AI can become educationally valuable: as a controlled rehearsal space before human communication.
AI Speaking Practice for English Learners: A Four-Stage Model
Rather than treating an AI speaking partner as a standalone activity, I would place it inside a four-stage learning sequence.
1. Prepare
Learners first understand what they are trying to achieve.
The communicative purpose, useful language and success criteria are made clear.
2. Rehearse with AI
Learners practise within a defined role or situation.
They can experiment with expressions, ask for clarification, repeat difficult parts and receive focused feedback.
3. Transfer to Human Interaction
This is the crucial stage.
Put the device aside.
Learners now complete a similar — but not identical — task with another learner or group.
The question therefore changes from:
“Could the student talk to the AI?”
to:
“Could the student transfer what they practised to communication with another person?”
4. Reflect
Ask learners three simple questions:
What new expression did I use?
Where did I struggle?
What will I do differently next time?
This small step turns “we used AI today” into an identifiable learning process.
Four Principles for Better AI Speaking Practice
AI speaking practice for English learners becomes more effective when AI is given a clear and limited role within the task.Give AI a narrow role
Instead of “Talk to me in English,” specify the interaction.
For example:
“Act as a hotel receptionist speaking to a B1 learner. Ask only one question at a time. Do not interrupt. At the end of the conversation, give three short pieces of feedback.”
The technology is now serving the learning objective rather than determining it.
Don't correct everything immediately
Constant interruption can work against fluency.
If the objective is fluency, maintaining communication may be more important. If the objective is a particular language form, feedback can focus specifically on that feature.
AI can also be instructed to focus on only two or three target areas rather than correcting every error.
Gradually remove support
During the first attempt, learners might receive a role card, vocabulary bank and sentence starters.
During the second attempt, some support disappears.
Eventually, learners face a more open and less predictable interaction.
The objective is not for AI to think for learners. It is to help learners become increasingly independent speakers.
End with human communication
AI can be patient, available and willing to repeat the same interaction again and again.
Those are useful affordances.
Human communication, however, is messier.
We need to interpret intentions, negotiate misunderstandings, manage turn-taking and adapt to different communication styles.
So the success criterion should not be:
“How long did the learner speak to AI?”
A more useful question is:
“Did the rehearsal help the learner communicate more effectively with another person?”
What might this look like in a 40-minute lesson?
A simple structure could work:
5 minutes — PreparationIntroduce the communicative objective and success criteria.
10 minutes — AI rehearsalLearners complete short speaking interactions using defined roles.
15 minutes — Human interactionDevices are put aside and learners solve a related problem in pairs or groups.
5 minutes — ReflectionLearners identify one useful expression, one communication strategy and one area for improvement.
5 minutes — Whole-class feedbackDiscuss one question: “What transferred from the AI rehearsal to the real conversation?”
Conclusion: Design the Communication, Not the Technology
AI speaking partners may have a valuable place in English language learning.
Emerging research suggests that structured AI-supported speaking practice can support aspects of oral performance. But the evidence also reminds us that outcomes are not identical across learners, tasks or contexts.
The teacher's role, therefore, is not to maximise the amount of time learners spend talking to AI.
It is to design the transition:
AI rehearsal → thinking → adaptation → human communication.
Because the ultimate purpose of learning English is not to have a flawless conversation with a chatbot.
It is to communicate meaningfully with other people.
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