A talented Applied Learning Science Researcher and CEO of an AI-driven learning platform, Kristina Novik, on why meaningful innovation begins with understanding how people learn.
While “teacher-less” AI tutors promise effortless automation, a research study, “The Effects of Over-Reliance on AI Dialogue Systems on Students’ Cognitive Abilities: A Systematic Review,” by scholars from Australia, shows a different reality: AI tools often overwhelm students, weaken motivation, and only succeed when guided by real teachers. The real opportunity isn’t in fully automated classrooms, but in tools that follow how learning actually works and make educators’ jobs easier, not replace them.
To understand what it takes to build such systems, TechBullion turned to Kristina Novik, an Applied Learning Scienсe Researcher specializing in EdTech product architecture, founder and chief architect of TeachEd, an AI-enabled learning ecosystem designed to strengthen professional teaching practice. In this article, you’ll learn why today’s automation wave misses the mechanics of real learning, how research becomes the logic of a platform, and what it looks like when AI is engineered to elevate teachers rather than sideline them.
The Hidden Cost of “Teacher-less” AI
Over the past decade, EdTech has produced a wide range of platforms, each promising transformation. Yet many of them focus primarily on generating content and tend to evolve as collections of unrelated features. This has gradually shaped a narrative that frames AI as a potential stand-in for teachers, diverting attention from its far more promising role: enhancing the depth and quality of learning experiences.
Kristina Novik takes a different path. Drawing on her master’s degree with distinction from the University of Manchester, one of the world’s leading centers for education research, together with extensive experience in instructional design and EdTech product development, she approaches the problem from first principles. Her work begins with a simple question: What should a system do if its purpose is to make evidence-based teaching practical in everyday classrooms? On her original platform, she translates learning science into system rules: structuring lessons, guiding instructional flow, and shaping AI outputs so they meaningfully support educators decision-making. The result is technology built to enhance clarity and strengthen instructional judgment.
“In my view, the role of AI is to strengthen learning,” Kristina says. “When technology respects how people actually learn, it helps shape lessons more intentionally and gives educators the freedom to make better, more informed decisions.”
When Learning Science Meets Real-World EdTech
One of the enduring challenges in EdTech is translating what we know about how people learn into the actual logic of a product. A new study, “Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy,” shows that AI can significantly improve learning only when it is built on stable cognitive principles. Most EdTech tools, however, stop at generating content and never translate this research into the actual architecture of a system.
With extensive experience evaluating her colleagues’ work for rigorous academic journals, judging innovation hackathons, and membership in IEEE, an organisation that admits only highly accomplished specialists in engineering and technology, Kristina brought a rare depth of insight to this question:
“Reviewing hundreds of works makes the patterns in learning impossible to miss,” Novik explains. “Across disciplines and age groups, you see the same sequence: how understanding builds in steps, how attention shifts with each task, and how the right moment for practice can change the entire outcome. What this showed me is that learning follows a recognizable rhythm. When we understand that rhythm, we can design solutions that help both teachers and students move through it with much greater clarity.”
Seeing the Patterns That Shape Understanding
Work at the intersection of learning science and product architecture is still uncommon in EdTech. It requires understanding how people learn, and seeing how those dynamics play out in real classrooms.
As a leading co-author of the Web of Science–indexed, peer-reviewed study “Web-based Applications in Higher Education: Revolutionising Language Learning in the Digital Age,” Kristina Novik and her colleagues conducted research with 150 university students and 20 in-depth interviews. Their findings showed that learners benefit from digital tools when those tools support attention, reduce overload, and provide clear practice guidance.
“What stood out in our research is how much stronger learning becomes when educators have tools that bring genuine clarity to the process,” Kristina shares. “One educator told us that when a tool helped her see which ideas her students were holding onto and which ones were slipping, she could adjust her explanation in the moment and the entire class moved forward together. Experiences like this show that digital support works best when it gives educators a clearer picture of what is happening in their students’ minds. When educators feel they can rely on that insight, their instruction becomes more focused, and students respond with greater energy. For me, this highlighted an important direction for EdTech: giving educators a timely understanding of their learners’ needs so they can do what they do best.”
Building Tools That Think Like Educators
The next frontier in EdTech is finding practical ways to turn learning science into everyday instructional support, tools that carry research insights straight into the flow of classroom decisions. Stepping directly into this challenge, Kristina Novik developed an instructional framework that brings learning science off the page and into the design of digital teaching tools. Drawing on her background in applied learning science and years of hands-on work across EdTech projects, she created TeachEd as a system that reflects the instructional realities she observed in classrooms.
The project distinguishes itself by building the logic of a real lesson directly into its core. Instead of producing isolated materials, the system follows the same instructional cues educators rely on: moments when attention rises, when ideas benefit from reinforcement, and when learners are ready for the next step. This research-driven structure guides how the platform organises instruction and how its AI adapts support, giving educators a clear foundation for planning and reducing the effort required to coordinate multiple tools.
“Early pilots showed that its greatest strength lies in offering a tool that mirrors the way experienced educators naturally sequence learning, making their work more focused and easier to sustain,” Kristina adds. “What matters most to me is creating technology that helps educators act with confidence in the moments that shape understanding. When AI highlights emerging ideas, reveals where attention is shifting, or shows readiness for the next step, it gives educators a clearer sense of direction. That kind of support enriches the learning process for everyone involved. In the future, I see the most impactful EdTech growing from a deeper partnership between research, educators, and the systems that assist them.”
