For us, AI literacy means that the ODU community can engage with AI in a way that is informed, intentional and appropriate to their field. That includes understanding what these systems can and cannot do, using them in context, evaluating outputs and making sound decisions rather than relying on them blindly.
We approach this as an extension and evolution of digital literacy, not a replacement. Students, faculty and staff still need to know how to research, analyze, communicate and think critically. AI simply changes how those skills are applied.
We don’t define AI literacy in terms of observable capabilities alone.
Importantly, this is not one-size-fits-all. AI literacy in teaching and learning, AI in research, AI in operations and then within each field — like AI in healthcare, engineering, business or education — looks different. Our focus is on embedding it within disciplines so people understand how AI is shaping their specific field, not just learning it in isolation.
EDTECH: MonarchSphere is being positioned as the first AI incubator in higher education. What gap did you see that traditional academic programs weren’t filling? Why an incubator model specifically?
MALOGIANNI: MonarchSphere was created to address a gap we were seeing between learning about AI and actually applying it in meaningful ways.
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Traditional academic programs are very strong in building foundational knowledge, but they are not always structured to support rapid, cross-disciplinary experimentation tied to real problems. At the same time, organizations are trying to figure out how to adopt AI responsibly and effectively.
The incubator model gives us a way to bring those pieces together. It allows us to take real use cases — from industry, government, research or institutional operations — and work through them in a structured way with students, faculty and partners.
We were very intentional about not positioning this as a stand-alone lab. It is an applied environment where ideas can be scoped, tested and refined before they move into implementation. That was the gap we were trying to fill.
EDTECH: There are many campus innovation labs. What makes MonarchSphere fundamentally different from those spaces? What does it enable that a typical lab or classroom can’t?
MALOGIANNI: Many institutions have innovation labs or makerspaces. Those are valuable, but they are often focused on exploration or technology exposure.
MonarchSphere is designed as an applied ecosystem rather than a physical space. It is connected to academic programs, research initiatives, institutional operations and external partners.
What makes it different is the focus on real use cases and the full lifecycle of work. We are not just asking, “What can this technology do?” We are asking, “What problem are we solving, and how do we do it in a way that is usable, responsible and scalable?”
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It also creates a structure for collaboration across disciplines and with both internal and external partners, which is difficult to achieve in a traditional classroom setting.
EDTECH: Can you walk us through a specific example of a project or use case coming out of MonarchSphere? What problem is being solved, and what are students building?
MALOGIANNI: One example that reflects this approach is our work around student pathways and advising.
Like many institutions, we see that students can struggle to connect what they are studying with where it leads. Through MonarchSphere, we are exploring how AI can support that process in a more integrated way, helping students better understand pathways, options and decisions.
What is important is not just the technology itself, but how it is designed and used. Students are not only interacting with these tools, but they are also thinking through the logic behind them, how recommendations are generated, what data is appropriate to use and what makes an output trustworthy.
At the same time, that is only one category of work. In MonarchSphere, students are also actively building and working with AI models in applied contexts. In engineering-related use cases, that may involve working with data to support modeling, simulation or optimization problems. In healthcare-related contexts, it involves exploring how AI can support analysis, decision support or workflow improvement, always within appropriate boundaries.
We are also beginning to integrate courses and structured learning experiences within the MonarchSphere environment, where students can move beyond theory and engage directly with applied problems, tools and data sets.
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The common thread across all of these is that the work is grounded in real use cases. Students are not working on abstract exercises, they are engaging with problems that require both technical understanding and judgment, which is ultimately what prepares them for how AI will be used in practice.
EDTECH: How are students engaging with AI differently inside MonarchSphere compared with a traditional classroom setting? What skills or mindsets are they developing that wouldn’t emerge otherwise?
MALOGIANNI: In a traditional classroom, students are often working within a defined structure with clear expectations and outcomes.
Within MonarchSphere, students are engaging with open-ended problems, working with others across disciplines and navigating uncertainty.
That changes how they engage. They are not just completing assignments. They are helping to frame problems, test ideas, evaluate outputs and think about how something would actually be used.
The skills that emerge from that are different. They develop comfort working with real life settings, stronger judgment and a better understanding of how technology fits into real workflows.
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