Whether you're leading an institution, building a product, or redesigning a course, the same question applies: did you start with the learner?
Most of the systems, tools, and processes in education were built around institutional structures, procurement cycles, and technology that made sense at the time. Now, the infrastructure is changing quickly. The world is moving even faster.
People making real progress right now, whether inside a university, a product team, or a single classroom, share something in common. They stopped building from the institution or the technology outward and started building from the learner inward.
In this edition of our newsletter:
Isabelle Hau, Executive Director of the Stanford Accelerator for Learning, makes the case that learning science belongs at the start of product development, not the end. She introduces relational intelligence and asks every product team a simple question: does your technology strengthen relationships, or replace them?
I lay out what university leaders are facing right now, five converging forces that make standing still the riskiest thing an institution can do, and a 90-day blueprint for leaders ready to act.
In our latest EDiT Talks, Professor Eric Tschirhart of the University of Luxembourg rebuilt his courses from learning objectives up, scoped the AI to what students actually needed, and changed how he assesses them.
Here's our challenge for you: start with the learner and design outward from there. We hope you get the inspiration you need to begin.
Warm regards,
Pablo Langa
Founder & Managing Partner
EDT&Partners
● The EDiT Spotlight: Isabelle Hau On Why Learning Science Belongs At The Start, Not The End
Isabelle Hau, Executive Director of the Stanford Accelerator for Learning, author of Love to Learn: The Transformative Power of Care and Connection in Early Education, and former impact investor at Omidyar Network and Imaginable Futures, on why learning science belongs at the start of product development and why relational intelligence is the capability the AI era demands.
1. When it comes to building EdTech products that genuinely reflect what learning science tells us, what does that process look like when it's done well?
The biggest mistake is treating learning science as something you consult at the end of product development. It needs to be there from day one.
The best teams start with a learning problem, not a technology. They bring together researchers, educators, designers, students, and engineers from the outset. They prototype, test, measure, iterate, and remain humble enough to change course when the evidence tells them to.
At the Stanford Accelerator for Learning, we often say that breakthrough educational innovation happens at the intersection of rigorous research, thoughtful design, and real-world implementation. When those three stay connected, technology becomes much more than software. It becomes a catalyst for better learning.
2. You talk about relational intelligence as the skill of the AI era. For an EdTech product team sitting down to build something, what does it actually look like in practice?
One of the strongest findings from the learning sciences is that we don't simply learn alone, we learn with and through other people. Relationships shape not only what we know, but who we become.
Relational intelligence (RQ) is the capacity to build trust, communicate across differences, collaborate, repair conflict, and create belonging. As AI becomes increasingly capable, these human capacities become even more valuable.
So I would ask every product team one simple question: Does your product strengthen relationships, or quietly replace them?
The most exciting AI products don't simply personalize learning for individuals. They help teachers know students better. They spark richer conversations among peers. They strengthen partnerships with families. They give educators more time for mentoring, coaching, and human connection.
Technology should amplify relationships, not compete with them.
3. You've referenced the AEMS framework — Active, Engaging, Meaningful, Social — as a way to evaluate whether technology is helping or getting in the way. If an EdTech company applied that filter honestly, what do you think they'd find?
One of the most influential contributions from my dear colleague Kathy Hirsh-Pasek is the AEMS framework: learning should be Active, Engaged, Meaningful, and Social. I think every education company should use it as a design checklist.
If many products applied that filter honestly today, I suspect they'd score well on engagement. AI is becoming remarkably good at capturing attention.
Far fewer would score highly on meaningful learning, or especially on the social dimension. We've spent years optimizing learning for individuals, yet many of the capacities that matter most for life develop through interaction with other people. Think collaboration, empathy, leadership, communication, or even curiosity and creativity.
As I think about the future, I sometimes wonder whether we should modify the last letter or add a new one: R for Relationships. Not because it replaces Kathy's framework, but because AI raises a new design question: Does this technology strengthen human relationships, or replace them? That may become one of the defining questions for education in the AI era.
4. The evidence for investing in early childhood is overwhelming. How do you read the current state of funding and EdTech development in that space?
The science has been remarkably consistent for decades. Early relationships shape brain development, language, executive function, mental health, and lifelong learning. We know that the earliest years generate some of the highest returns of any educational investment. Yet our investments still don't reflect that evidence.
At the same time, we should be thoughtful about where technology belongs in early childhood. Young children learn primarily through responsive interactions with caring adults, not through screens. The goal shouldn't be more technology for children. It should be better technology for the adults who care for them.
I see tremendous opportunity for AI to support parents, caregivers, and educators: helping them understand child development, suggesting age-appropriate activities, reducing administrative burdens, identifying developmental concerns earlier, and giving them more time and confidence to engage in rich, back-and-forth interactions with children.
In other words, the most promising use of AI in early childhood is not to replace human relationships, but to strengthen them. If we design technology that helps adults be more present, more responsive, and more connected to children, we'll be using AI in service of what the science has told us all along: relationships are the foundation of learning.
5. When you're evaluating whether something is worth scaling, what tends to separate the innovations that reach learners at scale from those that don't?
The first thing I look for is whether the team has fallen in love with a technology or with a learning challenge. The latter almost always wins.
The innovations that scale solve a meaningful need, demonstrate evidence that they improve learning, fit naturally into educators' workflows, and are designed with the realities of schools in mind.
The best founders also listen exceptionally well. They treat teachers, students, and families not as users, but as co-designers.
Ultimately, scale isn't about reaching millions of users. It's about improving millions of lives.
● The EDiT Spotlight: Isabelle Hau On Why Learning Science Belongs At The Start, Not The End
Isabelle Hau, Executive Director of the Stanford Accelerator for Learning, author of Love to Learn: The Transformative Power of Care and Connection in Early Education, and former impact investor at Omidyar Network and Imaginable Futures, on why learning science belongs at the start of product development and why relational intelligence is the capability the AI era demands.
1. When it comes to building EdTech products that genuinely reflect what learning science tells us, what does that process look like when it's done well?
The biggest mistake is treating learning science as something you consult at the end of product development. It needs to be there from day one.
The best teams start with a learning problem, not a technology. They bring together researchers, educators, designers, students, and engineers from the outset. They prototype, test, measure, iterate, and remain humble enough to change course when the evidence tells them to.
At the Stanford Accelerator for Learning, we often say that breakthrough educational innovation happens at the intersection of rigorous research, thoughtful design, and real-world implementation. When those three stay connected, technology becomes much more than software. It becomes a catalyst for better learning.
2. You talk about relational intelligence as the skill of the AI era. For an EdTech product team sitting down to build something, what does it actually look like in practice?
One of the strongest findings from the learning sciences is that we don't simply learn alone, we learn with and through other people. Relationships shape not only what we know, but who we become.
Relational intelligence (RQ) is the capacity to build trust, communicate across differences, collaborate, repair conflict, and create belonging. As AI becomes increasingly capable, these human capacities become even more valuable.
So I would ask every product team one simple question: Does your product strengthen relationships, or quietly replace them?
The most exciting AI products don't simply personalize learning for individuals. They help teachers know students better. They spark richer conversations among peers. They strengthen partnerships with families. They give educators more time for mentoring, coaching, and human connection.
Technology should amplify relationships, not compete with them.
3. You've referenced the AEMS framework — Active, Engaging, Meaningful, Social — as a way to evaluate whether technology is helping or getting in the way. If an EdTech company applied that filter honestly, what do you think they'd find?
One of the most influential contributions from my dear colleague Kathy Hirsh-Pasek is the AEMS framework: learning should be Active, Engaged, Meaningful, and Social. I think every education company should use it as a design checklist.
If many products applied that filter honestly today, I suspect they'd score well on engagement. AI is becoming remarkably good at capturing attention.
Far fewer would score highly on meaningful learning, or especially on the social dimension. We've spent years optimizing learning for individuals, yet many of the capacities that matter most for life develop through interaction with other people. Think collaboration, empathy, leadership, communication, or even curiosity and creativity.
As I think about the future, I sometimes wonder whether we should modify the last letter or add a new one: R for Relationships. Not because it replaces Kathy's framework, but because AI raises a new design question: Does this technology strengthen human relationships, or replace them? That may become one of the defining questions for education in the AI era.
4. The evidence for investing in early childhood is overwhelming. How do you read the current state of funding and EdTech development in that space?
The science has been remarkably consistent for decades. Early relationships shape brain development, language, executive function, mental health, and lifelong learning. We know that the earliest years generate some of the highest returns of any educational investment. Yet our investments still don't reflect that evidence.
At the same time, we should be thoughtful about where technology belongs in early childhood. Young children learn primarily through responsive interactions with caring adults, not through screens. The goal shouldn't be more technology for children. It should be better technology for the adults who care for them.
I see tremendous opportunity for AI to support parents, caregivers, and educators: helping them understand child development, suggesting age-appropriate activities, reducing administrative burdens, identifying developmental concerns earlier, and giving them more time and confidence to engage in rich, back-and-forth interactions with children.
In other words, the most promising use of AI in early childhood is not to replace human relationships, but to strengthen them. If we design technology that helps adults be more present, more responsive, and more connected to children, we'll be using AI in service of what the science has told us all along: relationships are the foundation of learning.
5. When you're evaluating whether something is worth scaling, what tends to separate the innovations that reach learners at scale from those that don't?
The first thing I look for is whether the team has fallen in love with a technology or with a learning challenge. The latter almost always wins.
The innovations that scale solve a meaningful need, demonstrate evidence that they improve learning, fit naturally into educators' workflows, and are designed with the realities of schools in mind.
The best founders also listen exceptionally well. They treat teachers, students, and families not as users, but as co-designers.
Ultimately, scale isn't about reaching millions of users. It's about improving millions of lives.
● What Kind of University Will Your Students Need? An AI Transformation Blueprint
Pablo Langa, Founder & Managing Partner, EDT&Partners, has spent over a decade working directly with universities navigating digital transformation. In this edition of The EDiT Digest, he asks the question every university leader should be asking right now: what kind of institution will your students actually need you to become? His blueprint is for leaders who are done waiting and ready to act, with real deployment data and a 90-day plan to start.
● What Kind of University Will Your Students Need? An AI Transformation Blueprint
Pablo Langa, Founder & Managing Partner, EDT&Partners, has spent over a decade working directly with universities navigating digital transformation. In this edition of The EDiT Digest, he asks the question every university leader should be asking right now: what kind of institution will your students actually need you to become? His blueprint is for leaders who are done waiting and ready to act, with real deployment data and a 90-day plan to start.
Standing still feels safe. Right now, it is the riskiest thing a university can do.
The forces reshaping Higher Education create a perfect storm. Demographics, governance gaps, new competitors, a shifting labor market, and AI adoption that has already outpaced institutional response.
The question is: will your university lead that change or be changed by it?
5 Forces creating the perfect storm in Higher Education
No single force explains the pressure. Several are arriving together, and the numbers behind them are hard to argue with.
Student profiles are changing. OECD data has tracked declining math, literacy, and science results since 2015. More than a third of students entering university now face well-being or mental health challenges. Universities are receiving weaker academic baselines and greater support needs at the same time.
Adoption has outrun governance. Ninety percent of Higher Education stakeholders already use AI, whether sanctioned or not. Yet only 19 percent of universities have a formal AI policy.
The university is being unbundled. In April 2026, the Khan-TED Institute announced a roughly $10,000 global bachelor's degree with no university behind it, set to launch in 2027. Teaching, credentials, community, and career access can now be picked apart and sold on their own.
"Job-ready" has been redefined. Entry-level roles increasingly expect what used to count as mid-level expertise, and growth is concentrating in AI-related and AI-enabled companies.
What holds its value is human judgment. The World Economic Forum's Future of Jobs Report 2025 names the durable skills: analytical, creative, and systems thinking, curiosity, resilience, empathy, and AI and data fluency.
The implications run deeper. These should change how universities teach, what you teach, what AI fluency and proficiency actually mean, and whether your university is itself an example of safe and effective AI adoption.
Build your capabilities in three layers
Any transformation worth the name rests on three layers working together.
Value alignment gives you the leadership roadmap, the public policy, and the dedicated budget.
Delivery capabilities are the enterprise, community, and learning engine work where AI is actually deployed.
Change management is what turns pilots into institutional practice: adoption at scale, reconfigured processes, and impact measured in KPIs that matter.
Each of those delivery dimensions is already showing what becomes possible:
As an enterprise. An AI admissions evaluator processed 1,240 applications with review time cut by 92 percent, from roughly 16 hours to 75 minutes. The hours it hands back go to the work that cannot be automated: the human judgment of whether a candidate fits.
As a community. An institutional prompt library places curated, values-aligned prompts inside the tools people already use. Faculty and students contribute their own and vote on their peers' contributions. A top-down mandate becomes a shared practice.
As a learning engine. An early-alert system monitoring 1,248 students flagged 36 at risk and reached them within 28 minutes on average through WhatsApp, with 64 percent completing their support plans.
AI blueprint preview: Give yourself 90 days
Working backward over three 30-day stretches:
Days 1 to 30 — see clearly. Map where your students and staff already use AI, without judgment, and pick two domains to reimagine.
Days 31 to 60 — build trust. Publish a responsible-use posture rather than a rigid policy, run curiosity-based literacy sessions, and choose two or three pilots in those domains.
Days 61 to 90 — prove value. Launch one visible pilot with defined outcomes, share results openly, and update your growth plan for the next nine months.
Institutions usually fall into one of three categories: ride in the passenger seat while faculty and students adopt AI around you, bet your strategy on procuring a single frontier tool, or become the university your students will genuinely need in an AI world.
In the full article, I show how to take a broad ambition like "student success" and break it down into outcome levers, solutions, and buildable use cases, the practical engine that makes this a working blueprint rather than a set of principles.
●The EDiT talks. Finding the Cowboys: How Specialized AI Is Rewriting the Rules of Higher Education
In our latest EDiT Talks, Laurie Forcier, VP of Strategy at EDT&Partners and Professor Eric Tschirhart from the University of Luxemburg discuss why universities need policies and people willing to experiment and prove what works.
Eric also shares his AI implementation journey. From watching his students struggle with generic AI tools, to rebuilding his courses from scratch and working with EDT&Partners to implement Lecture, an open-source GenAI framework designed for education.
Scaling with Intent: What APAC Needs from EdTech Companies
Kanaga Sundari draws on decades of experience to break down what APAC expansion actually requires, with practical examples from her own client stories.
AI & Accessibility Compliance: 5 Questions Every Publisher Needs to Answer Before the Deadline
Ana Isabel Calvo explains why most publishers treat accessibility as a legal problem and how treating it as a content, technology and strategy problem is the way to go.
