The Pulse of Higher Ed

By Vickie S. Cook, Ph.D.

Artificial intelligence is becoming part of the everyday work of higher education. From enrollment management and student services to academic administration and continuing and professional education, AI is creating opportunities to rethink how work is accomplished.

Much of the discussion has focused on what AI can do. For institutional leaders, however, a more important question may be emerging: How should people and AI work together?

In September, our inaugural Leadership Conversation with Ray Schroeder explored the importance of awareness, curiosity, and continuous learning. This month, we take the next step: translating that awareness into decisions about people, work, and organizational capability.

To explore these questions, I recently had the opportunity to speak with Dr. Irma Becerra, President of Marymount University. Her scholarship in knowledge management, engineering background, and experience in university leadership offer valuable perspectives on how institutions can strengthen human capability while maintaining judgment, accountability, and a strong commitment to student success.

Our conversation returned repeatedly to a central idea: AI’s value lies not simply in what the technology can accomplish, but in how it can help people become more capable.

Augmenting Human Capability

President Becerra offered a memorable analogy for understanding the relationship between people and AI. “AI is to your brain what eyeglasses are to your eyes.”  Just as eyeglasses extend our ability to see, AI can extend our cognitive capabilities. The purpose is not to replace human expertise but to help people recognize patterns, explore information, and accomplish work more effectively.

This distinction changes the questions institutional leaders should ask.

Instead of beginning with Which tasks can we automate?, we might begin with How could AI help our people accomplish work that matters more effectively?

President Becerra emphasized the importance of keeping the human expert involved in evaluating AI-generated information. AI may identify relationships or patterns that would otherwise require considerable time to discover. However, recognizing a pattern does not necessarily mean that the relationship is meaningful or that the resulting recommendation is appropriate.

Human expertise remains essential for interpreting information, identifying limitations, and determining what action should follow.

Consider enrollment management. AI may assist with organizing information, identifying patterns in prospective learner inquiries, or developing preliminary communications. Yet understanding a prospective learner’s circumstances, interpreting institutional priorities, and making consequential decisions about student support still require human responsibility.

Similarly, in continuing and professional education, which President Becerra refers to as “continuous learning,” AI may accelerate aspects of course development, market analysis, and administrative work. The value of these capabilities depends on how effectively educators and administrators apply their expertise to the resulting work.

The leadership opportunity is not simply to accomplish existing tasks faster. It is to examine how human expertise and AI capabilities can be combined to improve the quality of work, redefine shared work, and the experiences of those we serve.

Building an Organization That Learns

Introducing AI into an institution does not automatically create an organization capable of using it effectively.

President Becerra’s scholarship in knowledge management provides a useful perspective on this challenge. Knowledge management concerns how organizations create, capture, share, and apply knowledge. Its relevance to AI extends beyond access to information to the organizational processes and cultures that allow people to use knowledge productively.

Higher education institutions often approach AI development primarily through training: workshops, demonstrations, tutorials, and access to approved tools. These activities are important, but they may not be sufficient to develop an institutional strategy for integrating AI into the work of faculty and staff.

An institution can have employees who are individually proficient with AI while remaining organizationally unprepared to integrate it into shared work.

Our conversation also explored President Becerra’s engineering perspective on leadership and innovation. She described the importance of testing ideas, learning from results, and making adjustments rather than allowing extended analysis to prevent experimentation.

Her approach emphasizes rapid prototyping and creating an environment in which people can try new approaches, recognize mistakes, and use what they learn to improve.

For institutional leaders, this raises an important question: Are we creating conditions in which employees can experiment responsibly, share their experiences, and learn together?

Four organizational practices can help leaders create those conditions:

1. Communication across departments.
2. Opportunities for responsible experimentation.
3. Mechanisms for sharing successful practices and lessons learned.
4. Clarity about the institutional outcomes these efforts are intended to achieve.

These practices are particularly relevant to online, professional, and continuing education, where successful learner experiences depend on collaboration among academic departments, instructional designers, enrollment professionals, student support teams, and technology staff.

A recent UPCEA AI Hub case study illustrates how these principles can work in practice. The University of Arizona used AI to expand proactive student outreach while emphasizing collaboration and human oversight. The initiative demonstrates the importance of connecting AI-supported work to institutional needs and established professional responsibilities.

Leaders should consider not only whether employees have access to AI training, but whether their institutions provide opportunities for collaborative learning, responsible experimentation, and the development of shared practices that support institutional priorities.

Keeping Human Judgment and Accountability Central

As AI becomes capable of supporting more analytical and administrative work, leaders face increasingly important questions about judgment and responsibility.

The ability to generate a recommendation is not the same as having the authority or contextual understanding necessary to make a decision.

President Becerra emphasized the importance of maintaining an expert-in-the-loop approach. AI may help people recognize patterns and consider possibilities, but the human expert must evaluate whether the information is accurate, relevant, and appropriate for the situation.

This principle also applies to how individuals use AI in their everyday work.

President Becerra described using Microsoft Copilot to help refine her written communications.  As a continuous learner, rather than simply accepting the suggested language, she uses the interaction to consider how her messages might become clearer or more persuasive. The process provides an opportunity to strengthen her own communication skills while she retains responsibility for the final message.

This example illustrates an important distinction between using AI to complete a task and using AI to strengthen professional capability.

For higher education leaders, the implications extend beyond individual work practices.

When AI informs a decision affecting a learner, employee, or institutional resource, who is responsible for reviewing the information? How are errors identified? What opportunities exist to question or challenge an AI-supported recommendation? Are the people using these systems sufficiently prepared to recognize when human intervention is necessary?

The National Institute of Standards and Technology’s AI Risk Management Framework reinforces the importance of clear organizational responsibilities, appropriate human oversight, and accountability.

These are not exclusively technology questions. They are questions about organizational design, professional responsibility, and institutional trust.

Keeping people at the center does not mean rejecting AI capabilities. It means intentionally defining how those capabilities support, rather than displace, responsible human decision-making.

Preparing People for the Future of Work

One of the most significant themes in my conversation with President Becerra was the responsibility higher education institutions have to prepare students for a changing professional environment.

She observed that AI will reshape professions and create new opportunities, much as previous technological developments changed existing occupations and contributed to the emergence of entirely new fields.

Preparing students for that environment requires more than teaching them to use today’s AI applications.

President Becerra emphasized the importance of continuous learning and helping students develop the ability to adapt as technology and professional expectations change. Students need opportunities to understand AI’s capabilities and limitations, exercise independent judgment, and use technology ethically.

At Marymount, this commitment is reflected in efforts to incorporate AI into students’ educational preparation while establishing expectations for responsible use. President Becerra also described the university’s emphasis on internships, career readiness, and helping students understand the professional expectations they will encounter after graduation.

These efforts reflect a broader leadership philosophy: institutions should not leave students to discover essential expectations for success entirely on their own.

For leaders in online, professional, and continuing education, this perspective is especially relevant.

Many of the learners we serve are already participating in a changing workforce. They may be seeking new credentials, developing additional professional capabilities, or preparing for career transitions.

Our responsibility extends beyond helping learners become familiar with AI tools. We must consider how educational programs prepare them to exercise judgment, remain adaptable, and contribute meaningfully as their professions evolve.

This requires institutional leaders to remain continuous learners themselves.

Leaders Must Learn, Too

President Becerra’s approach to innovation reinforces an important leadership responsibility: leaders must model the curiosity, adaptability, and willingness to learn that they expect from faculty, staff, and students.

Institutional leaders do not need to become experts in every AI application. They do, however, need enough understanding to ask informed questions, recognize limitations, establish expectations, and evaluate whether AI-supported practices are improving institutional performance.

This requires a willingness to experiment, seek perspectives from others, reconsider established processes, and acknowledge uncertainty.

When leaders encourage responsible experimentation but discourage discussion of mistakes, they limit organizational learning. When they delegate all AI decisions to technology offices, they risk separating technology choices from institutional priorities. And when they focus exclusively on efficiency, they may overlook consequences for employees, the quality of institutional services, and ultimately, the learner experience.

Leadership in an AI-influenced environment is therefore not simply about managing technological change. It is about creating the conditions in which people can learn, exercise judgment, and adapt together.

Leadership Reflection

As I reflect on my conversation with President Irma Becerra, I keep returning to a familiar leadership principle: technology alone does not improve an organization. People, processes, and purpose must remain connected for an organization to be successful.

Her emphasis on augmentation, human expertise, responsible experimentation, and continuous learning reinforces the importance of approaching AI as an organizational leadership responsibility rather than simply a technology initiative.

We have seen this before in higher education. New technologies create opportunities, but meaningful institutional improvement requires more than implementation. It requires leaders to examine existing assumptions, engage the people doing the work, and determine what success should look like.

AI brings new capabilities and a faster pace of change, but it does not eliminate those responsibilities.

The more important leadership question may not be whether our institutions are using AI. It may be whether our people and our institutions are becoming more capable because AI is part of their work.

That question asks us to look beyond adoption rates and productivity claims. Are decisions improving? Are employees better supported? Are learners receiving better support and experiencing higher-quality learning? Are we creating opportunities for people to contribute their expertise in more meaningful ways?

Leading human–AI teams requires intentional choices about how work is designed, how knowledge is shared, where accountability resides, and how leaders support continued learning.

The objective should not be to create organizations in which AI does more work simply because it can. It should be to create organizations in which people can accomplish more meaningful work, with better outcomes for the communities we serve.

Questions for Your Leadership Team

As AI becomes increasingly integrated into institutional work, consider discussing:

1. Human capability: Are we approaching AI primarily as a way to automate tasks, or are we intentionally exploring how it can strengthen the capabilities of our faculty and staff?
2. Organizational learning: What opportunities exist for employees across departments to experiment responsibly, share experiences, and develop effective human–AI practices together?
3. Human judgment and learner preparation: How are we ensuring that AI strengthens professional judgment while preparing learners to work effectively in an AI-influenced environment?

Continuing the Conversation

Human–AI collaboration will continue to evolve as technology changes. The leadership challenge is to build institutions capable of learning, adapting, and making thoughtful decisions about how people and AI work together.

I appreciate President Becerra’s willingness to share her perspectives and experiences. Her emphasis on strengthening human capability, encouraging responsible innovation, and preparing students for the future provides valuable considerations for leaders throughout higher education. Read more on President Becerra’s leadership at Marymount University.

In November, Leadership Conversations in the Age of AI will explore a related question: Building AI Governance That Enables Innovation—How Should Institutions Make Decisions?

The technologies may change, but our responsibility to lead thoughtfully, ethically, and with institutional purpose remains.