Beyond One-Size-Fits-All: How AI Can Recognize Individual Knowledge Models and Support Every Learner
MathSense AI | Improving Math Learning for Every Student
9/18/20253 min read
Every educator has been there: facing a room full of students, each with wildly different needs, strengths, and struggles, and only one lesson plan in hand. It’s a daunting challenge — one that has defined modern education for decades. And it’s one of the reasons we started building MathSense AI.
We were solving a very critical problem: how do we support every learner in the way they actually need to be supported — when their path through knowledge is so personal, so varied, and so hard to see with traditional tools?
This question has shaped every decision we’ve made.
The Problem: A System That Can’t See the Learner
Despite decades of reforms, most students still struggle to meet grade-level expectations. According to the latest NAEP (Nation’s Report Card), roughly two-thirds of students in 4th and 8th grade are not proficient in reading or math. These aren’t just statistics. They represent millions of students not getting the support they need.
It’s a structural issue: our current system struggles to accommodate learner variability at scale.
Every student brings a different background, pace, and learning history. Yet we rely on fixed pacing guides and grade-level standards that assume uniform readiness. This results in learners being taught material they're not ready for—or being held back by content they've already mastered.
This disconnect has been well-documented in learning sciences. In fact, education theorists like Benjamin Bloom and Lev Vygotsky laid the groundwork decades ago for more personalized, mastery-based learning. But putting those theories into scalable practice has proven extremely difficult — until now.
The Core Idea: Recognize the Individual Knowledge Model
We designed our platform with one foundational belief: learning works best when we know what each learner understands — not just how they perform.
That’s why we use granular knowledge models — essentially maps that show how concepts are connected and how knowledge builds. These aren’t just sequences of content; they’re complex webs of interdependent ideas.
With the help of AI, we dynamically interpret a learner’s progress against this map. Our system can infer:
What the learner already knows
What misunderstandings may be blocking them
What they are most ready to learn next
This gives each learner their own personalized knowledge model—not a static profile, but a dynamic picture that grows and changes with every learning interaction.
For a deep dive into this approach, we recommend reading this article on Knowledge Space Theory and AI-Driven Personalization which explores the underlying theory our system builds upon.
Feedback That Builds Understanding
Timely and meaningful feedback is one of the most powerful drivers of learning. But most digital platforms focus on correctness, not understanding.
Our approach is different. Instead of simply saying “right” or “wrong,” our AI engine:
Diagnoses the likely cause of an error
Identifies whether it’s a misunderstanding or a missing prerequisite
Guides the learner to the most relevant, just-in-time support within their Zone of Proximal Development (ZPD)
This is how we help learners build knowledge, not just get through content.
For an excellent overview of how AI can support feedback loops in education, see The Personalized Learning Revolution: An EdTech Insider’s Perspective.
Why This Matters
We set out to create something that would genuinely support learning — not just at scale, but with care.
Because when we talk to educators, they don’t ask for fancier dashboards or more data points. They ask:
How do I know what my students really understand?
How can I give each one what they need without burning out?
How do I help a student who’s behind without leaving others waiting?
AI, when paired with learning science and grounded in strong pedagogical models, can help answer these questions.
➡️ You can learn more about how technology supports human learning and thinking in the article: Reimagining Education with Knowledge Models and AI.
Our Perspective
The future of education isn’t about more content, faster pacing, or higher standards alone. It’s about designing systems that see learners more clearly and respond more personally.
Artificial intelligence, used thoughtfully, allows us to do something we’ve never been able to do before: model a learner’s knowledge at scale and adjust instruction to match. This makes learning more efficient, more humane, and more effective.
That’s why we do what we do.
If you're an educator, technologist, or parent looking for a more personalized way to support learning, we invite you to explore this approach with us. Not because we have all the answers, but because we care deeply about asking the right questions—and building solutions that reflect the complexity and beauty of real learning.