From Fragmentation to Flow in Education

“Todd Brekhus on unifying data, AI, and curriculum to help teachers act on insights in real time. INTERVIEW | by Victor Rivero A longtime leader at the intersection of data, literacy, and personalized learning, Todd Brekhus has helped shape some of the most widely used digital tools in K–12 education. Now, as a leader at Renaissance, he’s focused on a far more ambitious challenge: bringing coherence to an increasingly fragmented edtech landscape. In this conversation with EdTech Digest , Brekhus discusses Renaissance Intelligence—a new, AI-powered “education intelligence system” designed to unify assessment, instruction, and practice into a single, actionable workflow. Drawing on decades of psychometric research, vast learning datasets, and long-standing educator partnerships, the platform aims to transform how teachers interpret data and respond to student needs—without losing the human touch that makes great teaching possible. For years, teachers have had to juggle multiple platforms to interpret student data. What was the core problem Renaissance Intelligence was designed to solve, and why is now the right moment for a unified “education intelligence system”? Educators today struggle with fragmented tools and disconnected data more than ever, due in part to the adoption of so many disparate platforms required to effectively deliver assessments, instruction, and practice. In typical classrooms, teachers now have access to as many as 60 edtech tools for instruction. Constantly switching between apps monopolizes teachers’ time and increases their cognitive loads. It also leads to less-than-coherent learning experiences for students, many of whom use digital practice programs whose scope and sequence don’t align well with the textbook they’re using. This is the problem Renaissance set out to solve. We believe coherence is the prerequisite to personalized learning. So, we are creating a solution that will help teachers to achieve greater instructional coherence—where all instructional resources, including assessment and practice, are organized to align with each district’s curriculum. To do this, we are combining decades of learning data and educator partnerships with responsible AI. ‘…we are creating a solution that will help teachers to achieve greater instructional coherence —where all instructional resources, including assessment and practice, are organized to align with each district’s curriculum.’ Renaissance Intelligence is the culmination of eight years of strategic acquisition and integration of digital learning tools. As the industry’s first Education Intelligence System, it is a category-defining model of integrated learning technology that helps districts personalize learning at scale. The system brings together data from our solutions across assessment, instruction, practice, and curriculum alignment into a single system and workflow for educators. Renaissance Intelligence bridges the gap between data and action, helping every teacher deliver grade‑level instruction with the right support at the right moment. Renaissance Intelligence brings together assessment, instruction, and practice into a single AI-driven workflow. How does integrating those pieces fundamentally change the way teachers make instructional decisions day-to-day? Renaissance Intelligence reshapes teachers’ workflows. It connects assessment insights to both math and literacy curriculum and relevant practice resources, so educators don’t have to stitch together tools or wait for the next data review to make decisions. Teachers and administrators no longer need to be nervous about whether a supplemental resource aligns with state standards or supports or distracts from the district’s curriculum. When the data says that a student is not ready for the next lesson, Renaissance Intelligence will present the prerequisite skills they need to work on and suggest relevant materials. Alternatively, when the data shows that a student already has skill mastery, it will make suggestions that enrich that student’s learning journey in close alignment with the curriculum. The system is the guide, enabling teachers to deliver data-driven, personalized instruction at scale in a way that a textbook alone cannot. The platform uses several specialized AI engines, from the Learning Engine to the Alignment and the Knowledge Check engines. How do these systems work together to move beyond reporting data to actually guiding instruction in real time? The Learning Engine is a foundational layer that enables the dynamic flow of information by blending assessment data with continuous practice data. It identifies students’ proficiencies, the skills they know, and their position on a learning progression. Then, based on our 40 years of learning research and insights, it determines which skills come next and generates a learning path for each student. The Alignment Engine makes this data relevant to the teacher in the context of their classroom and curriculum. It brings the students’ skills and skill gaps from the Learning Engine together with the right instructional materials. It reviews the core curriculum and related supplemental instructional materials—like Nearpod lessons or Freckle practice activities—and presents all the relevant materials to the teacher. Finally, the AI-powered Knowledge Check Engine offers tools to save teachers’ time. These AI features leverage dynamic data and classroom context to, for example, create rigorous formative assessments tailored to students or small groups. All of these engines work together to provide instructional coherence for educators, which Renaissance is uniquely capable of delivering because we’ve done work no one else has. We have built around 150 learning progressions, including reading and math learning progressions for every U.S. state. We understand standards and skills, and we have practice, instruction, and assessment solutions, so we can blend all these pieces to support teachers with data-driven guidance in real time, aligned to their core curriculum. ‘We have built around 150 learning progressions, including reading and math learning progressions for every U.S. state. We understand standards and skills …’ Many educators are excited about AI but also cautious. How did you approach designing Renaissance Intelligence around a “human-in-the-loop” philosophy so that teachers remain firmly in control? Simply put, our products must work for educators and students, they must work well, and educators must be able to trust the results. We all know that commonly used AI tools can have errors. But the stakes are higher in education, since a tool’s “hallucination” could interfere with a student’s learning. The tools and insights that are the foundation of our AI are grounded in our psychometric data and learning science research. And we always keep a human-in-the-loop at every step. For example, imagine that a student—let’s call her Olivia—recently completed a mid-unit assessment and has been practicing the unit skills for three days. Olivia’s teacher receives a quick summary of her recent performance with actionable recommendations to help her progress. It is up to the teacher to decide the best way to act; AI just provided the insights and resources to help. We want to be the teacher’s guide, so we clearly need to get it right. Renaissance Intelligence is not a replacement for teachers. We are focused right now on a teacher-facing model to ensure teachers can control students’ experiences. The AI acts as an expert partner, providing data and insights. It shows teachers which data points it used, but they have the classroom context, so they always get the final say. Renaissance has decades of assessment and psychometric research behind its products. How does that historical data foundation influence the reliability and credibility of the AI recommendations inside the platform? It’s easy to build an AI demo, but it’s incredibly challenging to build an AI product that is safe and performs well in classrooms. Our AI use is intentional and responsible, deeply grounded in psychometrics and learning science. Furthermore, our AI models are closed-loop systems that pull only from trusted data. We have decades of educational and pedagogical expertise on how students learn, the order in which they learn, and the relevant skill prerequisites. We also have billions of item responses across both our practice and assessment offerings that are de-identified from students but tagged to specific skills. We blend this de-identified data into a learning progression organized by skills, allowing us to cultivate a nuanced understanding of student learning and skill prerequisites. We also adapt AI recommendations based on context, as we have a rich algorithmic and AI-driven understanding of student learning in every state. With tools like AI Teacher Studio generating lesson ideas, quizzes, and differentiated materials, how do you envision AI changing the balance between teacher planning time and time spent directly with students? Renaissance Intelligence transforms teacher workflows and reduces the testing-to-teaching gap. Because Renaissance Intelligence is built on trusted data and decades of knowledge about student learning, it offers guidance that reflects the deep expertise of experienced educators and curriculum designers. The system’s generative AI tools draw on this data-rich environment, which stands in sharp contrast to standalone tools. Standalone AI tools tend to operate in data silos, increasing fragmentation and the potential for disconnect; in fact, from the teacher’s point of view, standalone tools are often “just one more app.” In contrast, Renaissance Intelligence’s AI tools are part of the system, which promotes instructional coherence in a way that siloed tools cannot. Let me illustrate this with an example: Suppose a teacher sees that they have six students with a particular skill gap. On the fly, the teacher can generate and customize an interactive lesson that’s personalized for these students, relevant to the classroom context, and aligned with the specific standard. This can all be done in minutes. Rather than spending precious time on lesson preparation and data synthesis, educators can redirect their attention back to where it matters most: interactions with students. Renaissance Intelligence gives teachers the time to sit beside learners instead of compiling data and building resources behind a screen, freeing them to coach, confer, and personalize learning in the moment. As one educator told us, it helps shift conversations “from talking about the data to truly talking about the student.” This creates a more collaborative dynamic, making students partners in their learning growth rather than passive recipients. You helped pioneer personalized reading with myON earlier in your career. Looking at Renaissance Intelligence today, how do you see AI reshaping the next era of personalized learning across both literacy and math? Earlier in my career, personalized learning meant giving students access to the right content at the right time—like matching readers to just‑right books. What’s different now is that AI can understand far more about each learner: their patterns, their misconceptions, and the exact skills they’re ready to take on next. It moves personalization from static and reactive to dynamic and predictive. ‘…AI can understand far more about each learner: their patterns, their misconceptions, and the exact skills they’re ready to take on next. It moves personalization from static and reactive to dynamic and predictive.’ Across both literacy and math, AI can connect signals that used to live in separate systems—assessment results, lesson performance, practice data, even indicators like attendance—giving educators a holistic view of each student’s needs. Instead of waiting for end-of‑unit tests or midyear diagnostics, teachers can intervene in the moment, with clarity about the skills that will make the biggest difference. Most importantly, AI elevates the teacher. It handles the pattern-spotting and heavy data synthesis so educators can focus on what humans do best: designing meaningful experiences, building relationships, and guiding students toward deep understanding. We’re entering an era where learning pathways truly adjust in real time, at scale, while keeping teacher expertise at the center. That’s the next frontier—and it’s far more powerful than anything we could have achieved a decade ago. — Victor Rivero is Executive Producer of Future Focus Forums (F3) and Editor-in-Chief of EdTech Digest. Write to: victor@edtechdigest.com The post From Fragmentation to Flow in Education appeared first on EdTech Digest .

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