Google and Meta have revolutionised marketing by turning data into performance analytics. Can something similar be done to ease the burden on teachers by making the process more data-driven?
This idea sparked the journey of Kushal Sinha and Piyush Kumar, the founders of Chicago-based LearnQ.ai, a smart learning platform. Their efforts culminated in VEGA (Virtual Entity for Guidance and Assistance), a specialised AI agent designed to guide and assist with any task.
It represents their effort to develop a teaching assistant that can support educators and institutions, all while maintaining the core AI and data-driven approach.
In an exclusive interview with AIM, Sinha, an IIT Guwahati alumni, said, “At this stage, AI is deeply integrated into the platform, consuming vast amounts of data to provide recommendations and insights. It thereby enhances the teaching experience and empowers educators with actionable intelligence.”
The duo wanted to take a focused approach. So, instead of going too broad, they decided to test the platform on a specific use case. “We chose the SAT exam, which is widely taken in the US for undergraduate admissions. That was our initial use case. Recently, however, we’ve started allowing early customers and clients to create their own courses,” Sinha added.
Now, they are enabling people to build courses tailored to their needs. In about a month, they’ll complete the rollout and open the platform to everyone, allowing users to build courses from scratch and deploy them.
“When we say “deploy,” the idea is to capture student interaction data—what they’re learning, where they’re struggling—and expose this data to both teachers and our AI assistant, VEGA.
“VEGA acts as an AI tutor or avatar, leveraging the student’s knowledge graph built through interactions, assessments, quizzes, and even chat history. It also integrates the professor’s expertise,” Sinha explained.
AI Assitant is Not AI Tutor
Kumar, the other co-founder and a former home tutor, told AIM that unlike traditional AI tutors, which often rely solely on prompts or pre-trained large models, their approach integrates real-time student data.
“For example, think of a smartwatch. If you’re self-motivated, you’ll act on its suggestions; if not, someone—like a family member—would nudge you. Learning works similarly. Some students are self-motivated, but most need additional support.”
Sinha mentioned that they use data pipelines to build a detailed knowledge graph for each student. This enables their AI system to cater responses to the student’s specific understanding level. It’s not just a wrapper around a large language model, it’s an agentic AI system that integrates data from multiple streams to deliver personalised, actionable insights.
People are now creating courses for AP, JEE, and even unique topics like teaching the Mahabharata and Ramayana. “This highlights the flexibility of our platform. Institutions like The Doon School in India are already using it to teach K-12 students,” said Sinha. Another organisation they are actively in discussions with is Allen in India.
Why Edtechs Fail?
“The first generation of ed-tech platforms was largely driven by the rise of the internet. Platforms like Coursera and Udemy focused on accessibility. They allowed teachers to record lectures and broadcast them widely, building platforms that connected learners to content,” Sinha mentioned.
According to him, these platforms didn’t address efficiency. Even with all the advancements in ed-tech, students relied on tutoring services, marketplace-based learning models, and physical tuition centres.
These platforms were more of an add-on rather than a replacement or an efficiency enhancer in education. That’s where LearnQ.ai’s approach differs, as they aim to solve for efficiency in education.
What’s Next?
Recently, Sinha and Kumar met with American entrepreneurs working with public schools in several states, who narrated a compelling success story. Four years ago, their schools ranked last in the district.
However, by implementing a data-driven approach and using simple Excel sheets, their performance saw a sharp improvement, pulling them to the top spots in the district. Most students now score within a tight 70 to 90 range.
That explains how any small technological adoption in the education system can bring vast improvements in a student’s performance. With AI, things are looking even better.
In the current landscape, many AI tutors lack a robust data layer. A truly effective one must be able to understand where each student is in their learning journey and provide tailored guidance. This becomes specifically important since teachers often struggle to give personal attention to each student due to time constraints.
For instance, if you’re watching a lecture on Coursera and don’t understand a concept, there’s no way to directly ask the instructor for clarification. This is where AI avatars for teachers and virtual assistants that provide 24/7 access step in.
Another much-needed AI intervention would be a feature to transcend language barriers. For example, a teacher who speaks only Hindi and English can have an avatar that interacts in Korean or Mandarin. This would dramatically expand accessibility, allowing the same educator to engage with thousands of students simultaneously, addressing doubts and providing real-time support.
Another upcoming feature empowers educators and creators to design their own AI tools, which will align with their unique processes and expertise. For instance, a blogger could train the AI to replicate their specific writing style or workflow using a knowledge base they provide.
These customisations open up endless possibilities for personalisation and efficiency across educational and creative fields.
