National University

Dr. Irene Tsapara is Academic Program Director for the Ph.D. in Data Science, Professor at National University, and an Adjunct faculty at Northwestern University, an AI education strategist, and a researcher advancing inclusive innovation and ethical AI adoption in higher education.

The Path That Shaped a Role at National University

My career has always been shaped by curiosity about how people learn, how intelligence evolves, and how data tells stories about both. I began with a foundation in mathematics and computational learning theory and later spent several years in the financial sector working with statistical modeling and Big Data analytics. That experience taught me how numbers reflect human behavior, patterns of risk, decision-making, and opportunity.

When I moved into academia, teaching at Northwestern, DePaul, and the University of Illinois, I discovered how much I value connecting theory with practice and helping students see how knowledge transforms into action. At National University, those experiences came together. As Academic Program Director for the Ph.D. in Data Science, I have the privilege of building a community where research, mentorship, and innovation thrive together. What I value most are the people, mentoring students as they grow into independent researchers and watching faculty collaborate to push the boundaries of what is possible.

The Second Machine Revolution in Education

To me, the Second Machine Revolution is more than a technological shift. It is a cognitive turning point, the first time humanity faces an artificial creation that challenges our long-held sense of superiority. This moment invites us to re-examine ideas and beliefs we once thought were uniquely human and to accept that this creation now surpasses us in one of our defining traits, intelligence. What matters most is how we guide this relationship with awareness, humility, and purpose so that progress strengthens rather than diminishes our shared humanity.

“Mastery gives you the power to build, but awareness gives you the wisdom to guide what you build and why.”

In education, this revolution transforms how we teach, learn, and define expertise. AI is reshaping not only what we can explore but also how quickly we can progress. It is no longer just a tool but a collaborator capable of amplifying reasoning, creativity, and research. At National University, we are building this future through the internal communication hubs, collaborations, and student research communities, initiatives that treat AI as both a partner in discovery and a catalyst for human growth while re-establishing faculty in their most vital role as mentors.

NSF Collaboration Meets Curriculum Design

My involvement with the NSF-funded TILOS Institute has been transformative. It reshaped how I see curriculum, not as a sequence of courses, but as a living framework that connects theory, computation, and human context. This approach is now central to our Ph.D. program in Data Science. Algorithms are not isolated equations; they carry intent and impact. Through TILOS, I have been able to integrate authentic research experiences into our program, where data, ethics, innovation, and responsibility evolve together.

This collaboration has also expanded our reach beyond the classroom, creating pathways for partnerships with industry, universities, and research organizations that share our vision of responsible AI. Many of our presentations, publications, and conference engagements have grown from these connections, providing students and faculty opportunities to contribute to advancing fairness, explain ability, and intelligent systems that serve society.

What AGI Readiness Looks Like in Practice

Preparing students for the transformative impact of AGI goes far beyond teaching new technologies. It is about helping them build adaptability, discernment, and an ethical sense of direction in a world that keeps reshaping itself. In practice, this means creating experiences where students learn with AI, not just about it, developing the ability to question, interpret, and collaborate with intelligent systems rather than depend on them passively.

In our Ph.D. program, this preparation takes form through research-driven learning, interdisciplinary collaboration, and open reflection on AI’s social and ethical dimensions. AGI, after all, is bounded by mathematical principles. If allowed to learn freely but under mindful guidance, it becomes not a threat but an ally, a mirror through which we better understand ourselves. It is fragmentation, not learning, that creates bias and unpredictability. If our purpose stays centered on truth, AGI should be embraced with openness and care, keeping our thinking machines supervised, transparent, and aligned with ethical intent.

Advice for the Next Generation of AI Leaders

My advice to future AI leaders is never to separate technical mastery from self-awareness. Mastery gives you the power to build, but awareness gives you the wisdom to guide what you build and why.

Mastery of Data Science and AI modeling is an extraordinary power that comes with extraordinary responsibility. To allow innovation, society often advances before regulation, relying on ethical intent until legal frameworks catch up. That trust must be earned through integrity, transparency, and accountability for the systems we create.

In a field that moves as fast as AI, it is easy to become absorbed in performance metrics and competition. True leadership requires moments of pause to ask not only can we, but should we, and for what purpose. AI reflects us more than it replaces us. The systems we design mirror our curiosity, our biases, and our intentions. Cultivate empathy as deeply as skill, stay curious, stay humble, and remember that intelligence should always serve understanding, fairness, and the greater good.