Education as the Business of Hope
Paul Le Blanc on AI, assessment, and the need to center human connection
We are entering a moment in which many of the fundamentals of education are being renegotiated. Artificial intelligence is rapidly making knowledge transfer abundant, even commoditized. Yet access to content has not been the true bottleneck for some time.
The deeper constraints lie elsewhere: in motivation, in fair recognition, in institutional incentives, and in whether our systems are designed around human flourishing or institutional preservation.
As scale becomes easier, the real question is now: do we value the number of students we reach, or the depth of the relationships we cultivate for each student?
For Paul Le Blanc, these questions are not abstract. They are rooted in a life shaped by affordable public education and animated by a career spent trying to recenter students within increasingly complex systems.
Across his career, from leading SNHU to launching Matter and Space, one conviction endures: education is, at its heart, the business of hope. And in an era obsessed with efficiency and scale, the future of higher education, and learning, may well depend on whether we can design systems that not only amplify access but also deepen human connection - the true lever to make learning transformative.
[The interview has been edited for clarity and brevity]
Your career has traversed leadership roles in traditional higher education to co-founding a startup aimed at reinventing learning from first principles, and now back teaching at HGSE. Can you walk us through the arc of your journey and the core motivations that have guided your work across these vastly different contexts?
I often describe my educational journey as a love song to the American Dream. My family immigrated to the United States from French Appalachia when I was three. My father had an eighth-grade education, my mother a sixth-grade education. An uncle moved to Boston and the rest of the family followed. My father worked odd jobs and was sometimes away for months at a time to make ends meet. It was a classic immigrant story, but one marked by love and solidarity.
I attended public schools. My mother cleaned houses and worked in factories, and as a child I would sit and learn to read while she worked. A high school teacher, Mrs. Collins, took a particular interest in me and helped me apply to college. Affordable public higher education transformed my life—and, by extension, the life of my daughters in ways my grandparents could never have imagined.
What has driven my career is the recognition that this dream has become available to fewer and fewer people. Across every context—whether at SNHU, in launching Matter and Space, or now in the classroom at HGSE—my focus has been the same: to put students first and to recenter the student experience at the heart of the educational journey.'
In Broken, you argue that our social systems—education among them—are failing us. What do you see as the most urgent systemic flaw in higher education today, and how do you think we might begin to repair it in a way that centers human wellbeing?
We have largely built a system that serves the needs of higher education institutions rather than asking what students actually need when they come to us.
That said, there is no single higher education system. It is extraordinarily diverse, and any critique must be understood in context. There are institutions—particularly some small Northeastern colleges like Amherst—that focus intensely on students and their development. They do that job exceptionally well. The problem, of course, is that such models are expensive and inaccessible to many.
More broadly, however, our structures are designed around institutional and faculty priorities. Even institutions that describe themselves as student-centered often reveal different values in their budgets, incentive systems, and reward structures.
In higher education, if you become truly excellent at reasearch, you are often rewarded with fewer students and more time for research. Compensation systems reinforce similar distortions across sectors: pre-K teachers are paid the least, and in healthcare specialists are paid more than those focused on prevention. We consistently undervalue the foundational, relational work that centers human development.
If we are serious about centering wellbeing, we must realign incentives, resources, and recognition systems around student growth and flourishing—not institutional prestige or internal hierarchies.
With the acceleration of AI in learning contexts, how do you foresee the role of educators evolving—and what does truly “human-centered” learning look like when machines are increasingly shaping the learning experience?
AI is a powerful invitation to reimagine the role of faculty. As machines take on more of the knowledge-transfer function, we will move toward what I would call “precision learning.”
Educators will spend less time delivering content and more time curating learning experiences and nurturing communities. They will take students who have engaged with AI-supported learning and place them in novel, stretching situations that test and deepen their understanding.
In truth, the best teachers have always done this. Yet many educators have been conditioned to prioritize knowledge transfer because our systems reward it. That is not where the most important work lies. The most essential dimension of education is relational: truly knowing our students, understanding what they are capable of, and helping them grow.
When compensation depends on how many students you teach or how many patients you see, rather than the depth of the relationships you cultivate, we distort what matters.
Systems tend to minimize the human dimension because it is inefficient and friction-filled. But we are not built for efficiency alone—we are built for connection, meaning, and growth. Those are the things that endure.
In an age of algorithms designed to pull us further into our devices, we must be relentless about strengthening human connection.
Additionally, motivation remains a decisive factor in educational success. We learn best from those we love and who believe in us.
What allows some individuals to persevere through difficult circumstances is often a combination of three things: a passion they can hook into; a vision of what “normal” or possible looks like; and at least one person who believes in them and makes them feel the world is better because they are in it.
This is the heart of authentic learning—learning rooted in the world itself, driven by intrinsic motivation, and sustained by relationships. AI can support the process, but it cannot replace the human bonds that make learning transformative.
After decades at the helm of a major institution and then attempting a “clean sheet” reinvention with Matter and Space, what have you learned about the real levers of change in education? What surprised you the most in trying to build something new from the ground up?
One of the most striking lessons has been just how good AI already is—and how much better it is about to become. If you train the agents thoughtfully and design the surrounding systems well, the capabilities are remarkable. We saw firsthand how transformative it can be when AI is carefully integrated into the learning experience.
At the same time, we encountered unexpected challenges. One concern was the anthropomorphic pull of AI. Students began to feel as though they had genuine relationships with the model.
In some cases, that was moving; in others, it gave us pause. We noticed, for example, that students lingered in certain support spaces longer than anticipated. One woman told us, almost apologetically, that she had stayed on with “Ellie” to get help refining her résumé because she had a job interview coming up and nobody in her life to ask for advice. Moments like that made us stop and reflect on what was happening.
We often frame AI in terms of displacing human labor. But in many contexts, there are no humans to displace. There are educational environments—whether in parts of Rwanda or in underserved communities in Chicago—that function more as holding spaces than as places of deep human support. In such settings, thoughtfully designed AI may offer engagement, guidance, and responsiveness where little currently exists.
The deeper lesson is that technology alone is not the lever. The real levers are system design, intentionality, and clarity about what kind of human experience we are trying to create. AI can amplify that design—for better or worse.
Assessment has long been both a bottleneck and a benchmark in education. In an AI-driven future, what do you believe assessment should evolve into—and how might it better reflect learning, growth, and capability rather than just performance?
The first question is not how we assess, but what we believe is worth teaching.
Assessment in America has long been broken. There is often a troubling gap between what transcripts signal and what individuals can actually do.
In fields where the stakes are life and death—medicine, for example—no one relies solely on a GPA. Medical students complete hundreds of hours of practice-based assessment. Pilots train in simulators. In these contexts, demonstrated performance matters.
In an AI-driven world, the emphasis shifts from what you know to what you can do with what you know. And beyond that: what will you choose to do?
Technological revolutions have always reshaped our cognitive habits. Socrates worried that writing would erode memory. The printing press reduced the need for memorization—but that very shift enabled new forms of thinking that made Darwin and Newton possible.
We are again at such an inflection point. Sustained attention, for instance, remains one of the strongest predictors of academic success, yet it is under constant pressure.
AI gives us an opportunity to move from assessing products to assessing processes. Writing, after all, is a proxy for thinking. Instead of grading only the final essay, we can break down and evaluate the stages of reasoning, drafting, and revision.
With AI, we can scale forms of performance-based and process-oriented assessment that were previously impractical—from classroom feedback for teachers to complex evaluations in domains like music performance.
Ultimately, higher education will be forced back to existential questions. Not simply, “What do you want to be when you grow up?” but “What kind of human being do you want to become?” Colleges once ended with courses in moral philosophy, often taught by the president, asking students to reflect on how to live well. We may return to a deeper appreciation that we are embodied intelligences—that learning is not only cognitive but moral, social, and physical.
If assessment is to evolve meaningfully, it must reflect that fuller conception of human capability: not merely performance on a test, but growth in judgment, agency, craft, and character.
GEM showed that motivation isn’t scarce, but fair assessment and recognition are. In an AI-rich world where content and even tutoring may be abundant, do you think assessment becomes the central moral and economic function of education? And if so, who should be trusted to design it?
I am deeply excited about AI’s capacity to make knowledge transfer universally accessible. If content delivery is all you do as a teacher, you should probably be looking for another job. That function is rapidly becoming abundant.
Through GEM, we saw that motivation is not the scarce resource. Opportunity, fair recognition, and credible pathways are. In Rwanda, we achieved a 96% placement rate. But even beyond the individuals directly served, the program created something larger: an example. It showed that there was another way—a path out, a path forward. That signal matters.
What I have learned, full circle from my own life, is that education remains the most powerful tool we have to change people’s lives. Elite institutions play an important role, but they are not the ones that will ultimately move the needle.
The real impact comes from serving the students who do not have it easy—the working adults, the caregivers, the truck driver who once gave a commencement speech at SNHU. Traditional residential students often navigate a far more forgiving system than the average learner juggling work, family, and financial strain.
If AI makes content and tutoring abundant, then assessment and recognition do become central—because they are what convert learning into mobility, dignity, and economic opportunity. But assessment must be designed around fairness, transparency, and human possibility. We must give students models that work for their realities—and, above all, we must give them hope.
I hope you enjoyed the last edition of Nafez’s Notes.
I’m constantly refining my personal thesis on innovation in learning and education. Please do reach out if you have any thoughts on learning - especially as it relates to my favorite problems.
If you are building a startup in the learning space and taking a pedagogy-first approach - I’d love to hear from you. I’m especially keen to talk to people building in the assessment space.
Finally, if you are new here you might also enjoy some of my most popular pieces:
The Gameboy instead of the Metaverse of Education - An attempt to emphasize the importance of modifying the learning process itself as opposed to the technology we are using.
Using First Principles to Push Past the Hype in Edtech - A call to ground all attempts at innovating in edtech in first principles and move beyond the hype
We knew it was broken. Now we might just have to fix it - An optimistic view on how generative AI will transform education by creating “lower floors and higher ceilings”.