Last January, during the Future of Education Technology Conference (FETC), the team from油District 91心頭istration stopped by a small startup booth inside the expo hall.
Behind the booth were two founders: and .
One came from the world of education and digital learning.
The other specialized in Android systems, AI, and robotics engineering.
Sitting on the table between them was a small conversational robot designed for K-12 classrooms.
But the idea behind it addressed one of the fastest-growing instructional challenges facing school districts today:
How do schools give multilingual learners significantly more opportunities to actively speak English without increasing teacher workload?
That conversation at FETC eventually led to the creation of , a platform combining conversational AI, teacher insights, a web-based learning experience, and classroom robots designed for multilingual learners in K-12 schools.
But the real story was what started happening inside classrooms.
ELL Teachers Are Being Asked To Do The Impossible
Across the United States, multilingual learner populations continue to grow while districts face persistent staffing shortages, intervention demands, and increasing accountability pressure around language proficiency outcomes.
Many ELL teachers support students across multiple grade levels, classrooms, and proficiency groups simultaneously.
And despite major investments in educational technology over the past decade, one challenge remains extremely difficult to scale:
Speaking practice.
Students need frequent verbal interaction to build fluency, confidence, pronunciation, and academic language development. But in classrooms with 20 or 30 students, individualized conversation time is limited.
Teachers involved in early pilots described students who avoided speaking because they feared making mistakes publicly or lacked confidence interacting in English.
Traditional educational software helped with vocabulary and exercises, but teachers repeatedly described the same missing piece:
live conversation.
The founders began asking a simple question:
What if AI could help schools scale individualized speaking practice in the same way previous generations of EdTech scaled reading and math activities?
The Classroom Reaction No One Expected
The first classroom pilots started modestly.
Students could interact with conversational AI through a small desktop robot placed inside the classroom.
The objective was not to replace teachers.
It was to create more opportunities for interaction during the school day.
But teachers quickly noticed something unexpected.
Students who rarely participated during lessons often wanted to continue talking with the robot after activities ended.
Some students voluntarily practiced English during free periods.
Teachers reported that shy students appeared significantly more willing to speak because the interaction felt lower pressure than speaking in front of an entire classroom.
For younger learners especially, the embodied aspect changed the dynamic.
The interaction felt less like completing software and more like participating in a conversation.
Several educators described students treating the robot almost like a classroom companion rather than another academic tool.
That reaction stood out to many district leaders.
After years of deploying screen-based educational platforms, many schools are now facing a growing challenge:
student disengagement.
Teachers involved in the pilots often described students interacting with conversational AI differently than with traditional educational software.
The experience felt more active, social, and participatory.
Instead of clicking through exercises, students were engaging in real-time conversation.
For many educators, that shift may represent one of the most important opportunities for AI in multilingual learning:
turning language practice from a passive activity into an interactive experience.
Beyond Conversation: Helping Teachers See What They Cannot Track Alone
As pilots expanded, districts began asking another question:
Could conversational AI also help teachers better understand language development over time?
That question led to the development of Telo AIs Teacher Dashboard, which provides AI-generated insights specifically designed for multilingual learner instruction.
Instead of manually documenting every interaction, teachers can access visibility into:
- speaking participation
- vocabulary usage
- conversation frequency
- engagement patterns
- language progression over time
The platform was designed around multilingual learning frameworks already used by districts, including WI91心頭, Texas ELPS, New York multilingual learner frameworks, state ELPT standards, and California ELD standards.
Rather than asking educators to redesign instruction around AI, the system was designed to support existing instructional goals and proficiency frameworks already guiding multilingual programs across the country.
For many district leaders, this became just as important as the conversational experience itself.
The Bigger Shift District Leaders Are Watching
One of the biggest surprises for the founders was how quickly district conversations moved beyond robotics.
School leaders were not asking:
Is the technology impressive?
They were asking:
- Can this help address staffing limitations?
- Can this increase speaking opportunities without increasing headcount?
- Can this improve student confidence?
- Can this fit into real classroom workflows?
- Can this scale across schools with different instructional models?
Those questions ultimately shaped the platform far more than the technology itself.
Several districts also pushed the company to expand beyond robotics and offer a fully web-based experience that could run on existing student devices.
Today, districts can deploy Telo AI either through conversational AI robots or through a browser-based platform depending on classroom and staffing needs.
Some districts use the platform during station rotations.
Others use it for after-school tutoring, intervention support, newcomer programs, or elementary multilingual classrooms.
Both approaches are built around the same core idea:
creating more opportunities for individualized speaking practice without creating additional pressure on already overwhelmed teachers.
AI That Creates More Human Interaction
Much of the public conversation around AI in education focuses on automation.
But educators involved in these pilots often describe something different happening inside classrooms.
Conversational AI may actually help create more human interaction, not less.
Teachers spend less time trying to force participation and more time coaching students during meaningful conversations. At the same time, educators gain visibility into language development patterns that would otherwise be nearly impossible to track consistently at scale.
As districts continue exploring how AI fits into K-12 education, multilingual learning may become one of the first major proving grounds for conversational and embodied AI.
Because language learning is fundamentally interactive.
The founders believe the long-term opportunity is not replacing teachers, but helping them scale individualized interaction in classrooms where time, staffing, and instructional demands are increasingly stretched.
And sometimes, that transformation starts with a small robot sitting quietly on a classroom desk.
Learn more about conversational AI for multilingual learners at
For district partnerships, pilots, or product demonstrations, contact: [email protected]
