The Education Revolution: A Synergy of Governance and Innovation

 The goal of higher education has never been, and shouldn't be, to move faster.  But technology waves have always been both a stimulus and a challenge for rapidly scaling changes in higher education curricula. And in the case of emerging technologies like Artificial Intelligence, higher education is being challenged to rapidly produce AI natives (Stryker, 2026), savvy enough for the future of work and grounded enough to implement AI ethically and responsibly (McNair, 2026).  

Governance ensures that policy doesn’t chase trends, and that curriculum doesn’t lurch forward, blindly and clumsily, at every wave of technology. The deliberateness built into academic governance, and its adherence to proven andragogical pathways, are key to the success of higher education.

Innovation offers new opportunities for higher education to maintain its powerful governance architecture while incorporating nimbler pathways that support adaptive learning, enhance accessibility, and build workforce-ready students with the knowledge they need to meet the demands of AI-centric careers.

Two lanes, different speeds, different needs, different outcomes. The new Tortoise and the Hare?

This reminds me of the fable of the Tortoise and the Hare (Aesop, n.d.). In that fable, the Tortoise challenges the Hare to a race, and ultimately wins, with the lesson being that consistency and persistence are the keys to success.

But that's not the full story or the message here. In the version higher education needs, the Tortoise and the Hare aren't racing each other — they're running different races, each suited to different terrain. The Tortoise's pace fits terrain that rewards being deliberate: degree requirements, accreditation, anything the institution can't easily walk back. The Hare's pace fits terrain that rewards innovation: creative, nimble, and fit for purpose.

The two-lane pathway…reimagined.

In a previous teaching role, I proposed layering AI-domain content onto an existing Informatics course — a short AI-driven task added to units the course already had, without changing its learning objectives or triggering a full course development update. It’s low lift, measurable, and something another instructor can pick up without extensive training.

At UAGC, I've worked with my department chair on the same principle — bringing AI into the existing course structure without requiring the full deliberative cycle a curriculum change requires. Neither example bypassed governance. Both simply matched the process to the actual scope of the change, leaving the large-system decisions exactly where they belong.

Integrative learning by design.

Throughout my courses, I’m integrating AI governance and business tools into our discussion posts.  The architecture of Operations Management is built for this two-lane pathway.

• Systems analysis is the deep dive, reserved for decisions with real organizational or legal weight — degree requirements, accreditation-linked policy, anything that changes the institution's operating environment overall. This is where governance lives, and discussions of ethical and responsible AI policies are a natural fit.
• Process lifecycles are where continuous, iterative improvement lives — decision trees, pilot programs, guidance documents, Scrum lifecycles — for decisions that are designed for shorter, simpler, and faster solutions in smaller ecosystems.  Many students are already using large language models (LLMs) in their work to facilitate these lifecycles.  It’s easy to talk about the benefits and the challenges of these models in a context that has immediate meaning for them.
• Production control and audits — monitoring what's already running, catching drift early, making corrections in real time, before a small issue becomes a large one. AI is the harbinger of change.  But an organization can’t be constantly in motion.  This creates space to discuss best practices for harnessing AI-focused change with intention, not just speed.

These are the lanes most institutions haven't formally defined. Without that delineation, everything gets routed through systems analysis, whether it needs that scope or not, and the institution ends up moving at one pace for problems of very different sizes.

Higher Education is the driver for the pace of AI integrations, not the other way around.

AI is not the driver for the pace of higher education.

AI doesn't change what operations management is. It doesn't alter the discipline's core practices — systems thinking, process improvement, and weighing tradeoffs. What it does is open new doors within that same framework: new inputs to diagnose, new tools to map to the challenge at hand.

As these pilot applications of AI technologies evolve, by the time students graduate, they're already practiced at scanning an environment and recommending action-sized to the actual stakes involved, using AI tools with confidence. Students graduating with that instinct won't just adapt to AI's presence in modern business — they'll be equipped to govern its integration in higher education and everywhere else, where large systems are being asked to move at a new pace without losing what made them trustworthy in the first place.

The best solutions will always rest on the insight and nuance unique to human decision-making. Higher education's enduring value is in its ability to teach students how to master these tools in thoughtful, value-driven ways.

References.

Stryker, C. (2026, February 3). What is AI Native? IBM. https://www.ibm.com/think/topics/ai-native

McNair, K. (2026, May 18). As more jobs demand AI skills, some colleges may fall short in prepping students: "Why would we train them using the skills of yesterday?" CNBC. https://www.cnbc.com/2026/05/18/ai-workforce-college-jobs.html

Aesop. (n.d.). The hare and the tortoise. Read.gov. https://read.gov/aesop/025.html

 Bio:

Professor Karen Jensen

Karen Jensen is an AI ethics and governance advisor, digital transformation consultant, and educator whose work focuses on how organizations can adopt emerging technologies without losing the governance, human judgment, and institutional knowledge that make complex systems trustworthy. Her career spans higher education, private industry, government initiatives, and the nonprofit sector, with experience in organizational transformation, cybersecurity workforce development, and responsible AI. Jensen is an Adjunct Global Instructor at the University of Arizona Global Campus and has developed and taught curricula in business and information technology at UAGC and in previous roles. She has contributed to national cybersecurity workforce initiatives through NIST, served as a merit reviewer for the National Science Foundation and PCORI, and works with Women in AI’s Global Office of Ethics and Culture. She holds an M.S. in Management and Ethical Leadership and a B.S. in Information Technology Business Management.