Beyond the Hype: What Faculty Leaders are Teaching Us about AI in Higher Education

Bao Johri, Vice President for Information Technology and Chief Information Officer, California State University, Fresno

Bao Johri is Vice President for Information Technology and Chief Information Officer at California State University, Fresno. A senior higher education executive with more than 20 years of experience across public and private sectors, she leads enterprise technology strategy, IT investment and operations, cybersecurity and institutional innovation in support of academic excellence and student success. She is recognized for advancing responsible AI integration, strengthening governance and aligning technology investments with mission-driven outcomes across complex, regulated environments.

Artificial intelligence has moved from curiosity to catalyst in higher education. Across classrooms, research labs and administrative offices, AI tools are reshaping how work gets done and how learning is experienced. Yet beyond headlines about disruption and rapid transformation, I continue to return to a more grounded question: How do we integrate AI in ways that are ethical, meaningful and aligned with the core values of education?

At Fresno State, I launched a year-long initiative called The AI Conversation to explore that question through practice rather than prediction. As CIO, I host this webinar series to create space for faculty and staff to share how they are using AI in teaching, research and academic work. What has emerged is not a blueprint for rapid adoption, but a set of leadership insights about trust, culture, ethics and sustainable innovation.

Faculty Want to Learn from Faculty

One of the clearest lessons is simple: faculty trust their peers.

Rather than centering vendor demonstrations or technical workshops alone, the series highlights real-world applications from colleagues across disciplines—arts and design, art history, computer science, engineering, agriculture, kinesiology and health sciences. This cross-disciplinary format makes AI practical and contextual rather than abstract.

When a studio art professor explains how AI enhances creative practice, or an engineering faculty member shares how it supports modeling workflows, AI shifts from institutional directive to shared exploration. It becomes less about compliance and more about curiosity.

As one participant advised—we don’t need to become an AI expert overnight. Begin by exploring just one or two AI tools relevant to your discipline.

That message—start small, stay curious—has proven more effective than sweeping transformation narratives. Peer-led learning reduces anxiety and builds confidence in ways policy guidance alone cannot.

AI Literacy is about Critical Thinking

Institutional AI conversations often focus on platforms and policies—Which tool? What features? What guardrails?

Through The AI Conversation, faculty consistently reframe the issue. AI literacy, they argue, is less about mastering tools and more about cultivating critical thinking and ethical awareness.

One faculty member shared—for me, integrating AI isn’t just about teaching students how to use the tools; it’s about teaching them to critically analyze them, to understand their limitations and to use them ethically and responsibly.

“One of the most significant outcomes of The AI Conversation has been the normalization of open dialogue.”

That distinction is essential. AI systems are increasingly embedded in professional fields—from healthcare and agriculture to engineering and design. Our students must graduate prepared not only to use AI, but to question it—examining bias, transparency, authorship and reliability.

Rather than banning AI outright or embracing it uncritically, faculty are redesigning assignments to promote inquiry. Students compare AI-generated responses with human analysis, identify gaps or inaccuracies and reflect on ethical implications. In this approach, AI becomes a catalyst for deeper learning rather than a shortcut.

For education technology leaders, the implication is clear that innovation must strengthen analytical capacity and ethical reasoning alongside technical familiarity.

AI Integration is Deeply Disciplinary

A persistent misconception is that AI belongs primarily in STEM. Our experience suggests otherwise.

One arts faculty member described blending traditional techniques with generative tools—In my studio practice, I combine traditional materials such as acrylic and Korean ink painting, with AI technologies. I generate images using AI tools, transfer them onto canvas and integrate them through manual painting.

In this setting, AI expands creative expression rather than replacing it. In agriculture, it supports predictive modeling. In kinesiology, it assists with research interpretation. In art history, it becomes a lens for examining authorship and authenticity.

These examples reinforce a critical leadership lesson: AI adoption is shaped by disciplinary culture. One-size-fits-all strategies rarely succeed. Institutions that honor contextual differences while encouraging experimentation are more likely to foster meaningful innovation.

Ethical Dialogue Builds Trust

AI adoption raises legitimate concerns—academic integrity, data privacy, intellectual property, bias, equity and workload. Ignoring these issues risks eroding trust.

One of the most significant outcomes of The AI Conversation has been the normalization of open dialogue. Faculty share not only successes but uncertainties. They discuss evolving classroom policies and explore assignment strategies that encourage transparency rather than prohibition.

This openness signals that AI integration is not being imposed without reflection. It strengthens institutional credibility at a time when higher education faces heightened scrutiny and shifting expectations.

Education technology leadership today requires as much attention to values as to infrastructure. Ethical engagement must be central to innovation.

Moving beyond the Hype

The experience of The AI Conversation has reinforced a simple truth that the future of AI in higher education will not be determined solely by software platforms or infrastructure investments. It will be shaped by how institutions cultivate trust, foster ethical reflection and empower faculty leadership.

Meaningful innovation begins with people. When faculty are invited to share, question and collaborate, AI becomes less about disruption and more about possibility—grounded in values, strengthened by community and aligned with the enduring mission of higher education.

Beyond the hype, that may be the most important lesson of all.

AI

Information Technology

Scrutiny

Healthcare