
With the rise of Gen Alpha and Artificial Intelligence across every sector, education is undergoing significant transformation, and English language teaching is no exception. The way fluency is taught and assessed is being redesigned, not because the goal has changed, but because the learners and the tools have.
A generation immersed in AI arrives in the classroom with a learning profile that traditional teaching was never designed to meet. The challenge isn’t only technological. It’s pedagogical. How do we teach, assess, and develop fluency in students who grew up in a context no textbook anticipated?
It was within this landscape that we brought together experts to discuss the new reality that language schools and institutions are already navigating. We didn’t come with ready-made answers, but the insights that emerged from the conversation point toward meaningful ways of rethinking fluency development and assessment in today’s environment.
In this post, we’ve gathered the key points from that conversation: what was said, what was challenged, and what remains open. Explore the key takeaways below!
Until recently, individualized feedback at scale was simply not feasible. A teacher cannot assess the pronunciation of thirty students with depth, in real time, without compromising the quality of the analysis. AI solved that problem, but what do we do with that much information?
The panelists pointed to a shift that goes beyond technology. Gen Alpha students arrive in the classroom with exposure to a technology that previous generations never had. They grew up consuming and generating contentmaterial with AI. The starting point has changed. The way we teach and assess language needs to change too.
One of the most relevant moments in the panel was the deconstruction of the concept of fluency. The idea that speaking well means sounding like a native speaker no longer holds, and the experts were direct about it.
Fluency, today, is understood as communicative performance: conveying a message with cohesion, interacting with confidence, achieving a communication objective within a context.
Speech rate and rigid grammatical accuracy are no longer the be-all-end-all, especially in the early stages of learning. What matters first is the ability to communicate naturally and to build language range and accuracy from there.
“It is not just accuracy or that you sound native like, but use appropriate vocabulary, speak intelligibly and respond accordingly to context.”
– Allen Quesada
“I’d like to define in terms of being able to convey your message coherently, cohesively with a communicative purpose.”
– Adriana Salvanini
“Being able to interact with people from different cultures!”
– Isabela Villas Boas
AI delivers scale, consistency, and immediate feedback. But we also discussed something human feedback carries that no model can fully process: awareness of the surrounding real world context.
AI is a support tool that does not replace the teacher. The teacher remains fundamental to the educational process. For feedback to truly reach the student, it needs to be mediated and transformed into prescriptive guidance relevant to their learning path and educational context.
The model the panel defends is not AI or human. It is AI and human, each acting where it is most effective. AI processes, structures, and scales. The teacher interprets, contextualizes, and acts. This is what has become known as human-in-the-loop, and it seems to be the most strategic path for language teaching today.
One of the most common concerns among educators is the feeling that they need to master technology to remain relevant. That is not what is expected of the teacher.
What matters is understanding how to use the tools ethically, designing learning experiences, and asking the right questions in the classroom, considering the context of Gen Alpha. Curation and pedagogical judgment remain the teacher’s domain.
And there is an interesting development: when the teacher understands what AI does, they can also use it to develop students’ critical thinking. The tool stops being just a support resource and becomes part of the learning process itself.
“We don’t need to fall into the trap of wanting to become programmers. We don’t need that, we have to learn how to use AI for a pedagogical purpose.”
– Adriana Salvanini
The participating experts project a significant reduction in traditional assessment formats. Rigid multiple choice tests and gap-fill activities are expected to lose ground to performance-based assessments. We discussed systems that track student performance on a daily and continuous basis, making formal exams increasingly less necessary to define proficiency.
But there is one point no technology resolves: effort. Learning involves mistakes, attempts, cultural and cognitive experience. AI can expand access and the quality of feedback, but it cannot replace the learning process itself.
The greatest value of this one-hour conversation was not pointing to Artificial Intelligence as a magic solution, but as a mirror that forces us to look at our own pedagogical practices. Technology advances in large steps, but the essence of education remains intact: it is, fundamentally, a social, cultural, and emotional activity.
If Gen Alpha already arrives in the classroom operating from a new starting point, it is up to institutions to redesign the rules of the game. The future of language teaching will not be defined by who has the most advanced technology, but by who knows how to use it with the best pedagogical intention. AI takes on the mechanics of the process. The teacher, more strategically than ever, takes on the role of mentor in the educational journey.
We invite you to check out the highlights on LinkedIn and watch the full live on YouTube, and be part of this enriching conversation that brings reflections on the present and future of education.