Date of Graduation

Fall 12-31-2025

Document Type

Dissertation

Degree Name

Doctor of Education (Ed.D.)

College/School

School of Education

Department

Leadership Studies

Program

Organization & Leadership EdD

First Advisor

Desiree Zerquera, PhD.

Second Advisor

Ursula Aldana, PhD

Third Advisor

Danfeng Soto-Vigil Koon, PhD, JD

Fourth Advisor

William Bosl, PhD

Abstract

Artificial Intelligence (AI), especially Generative AI (GenAI), is transforming how we work, learn, acquire, produce, and deliver knowledge, as higher education experiences existential pressures, including demographic changes that reduce enrollment and revenues, as well as shifting public and government perceptions of higher education's value.

This hermeneutic phenomenology dissertation study explores how faculty early adopters critically engage, innovate, and experiment with this nascent transformational technology in their pedagogies amid multilevel pressures: macro—market-driven (neoliberalism) logic, Big Tech, and demographic change; meso—institutional policy, broader faculty community, and pedagogical innovation; micro—widespread student adoption, pedagogical experimentation, and innovation.

This dissertation study centers on shifting AI literature and practice, and on fifteen semi-structured, iterative, and interpretive interviews with faculty early adopters at a private, mission-driven university in Northern California, in the midst of the ecosystem shaping AI, from Fall 2024 to Fall 2025. T

he dissertation study pays special attention to Generative AI because it is the most widely adopted and visible form of AI at the time of this writing; however, the governance and ethical implications discussed here are intended to extend to emerging, advanced forms of AI including Agentic AI, which refers to AI systems with the capacity to take action by using tools and making decisions with limited human supervision, and Embodied AI, which refers to autonomous AI systems embedded in physical machines that are informed by sensor inputs to take real-time actions in the physical world.

This dissertation identified four intersecting themes: market-driven ideology embedded in AI and faculty pushback; student-wide adoption and ethical misalignment; institutional support gaps and faculty burden; and faculty sensemaking and pedagogical innovation.

The dissertation study contributes through a multilevel analysis of faculty sensemaking, role negotiation, and artifacts related to AI ethics and AI governance (use cases, pilots, and frameworks). It develops actionable recommendations to support critical, ethical, and safe integration into higher education—shifting narratives from AI-use detection and punishment towards critical engagement in pedagogy. The knowledge and artifacts provide higher education leadership and faculty with tools to assess when and how to engage AI, grounded in ethics, governance, safety, and human-centered principles.

Download

Share

COinS