Date of Award

6-2026

Degree Name

Doctor of Philosophy

Department

Educational Leadership, Research and Technology

First Advisor

Louann Bierlein Palmer, Ed.D.

Second Advisor

Jessica Heybach, Ph D.

Third Advisor

Kari Huckaby, Ed.D.

Keywords

Career construction theory, generative AI in higher education, Interpretive Phenomenological Analysis (IPA), student meaning-making, transformative learning theory, vocational discernment and career development in technology contexts

Abstract

This interpretive phenomenological analysis examines how 13 undergraduate students at a liberal arts college construct meaning from their experiences with artificial intelligence technologies in relation to their career development and life purpose. Guided by an integrated theoretical framework combining Career Construction Theory (Savickas, 2012, 2013) and Transformative Learning Theory (Mezirow, 1991, 2000), the study addresses three research questions: how students describe their AI experiences across educational contexts, what connections they make between those experiences and their developing professional identities, and how they integrate AI experiences into broader narratives about work and purpose.

Data are collected through phenomenological interviews with 13 participants representing diverse academic disciplines and class years. Cross-case analysis produces six superordinate themes: Identity as AI Regulator, The Cognitive Atrophy Continuum, Structural Drivers of AI Dependency, The Human Irreducible, AI as Mirror Not Shaper, and Meaning Erosion as Emerging Professional Concern. Collectively, these themes demonstrate that students interpret AI experiences through vocational lenses, that AI encounters function as occasions for professional identity construction, and that the integration of AI into career narratives is shaped by both individual meaning-making processes and structural conditions that constrain individual choice.

The study's theoretical contributions include the extension of Career Construction Theory to encompass AI encounters as career construction occasions, the identification of reflective consolidation as a distinct outcome within Transformative Learning Theory, and the articulation of meaning erosion as an emerging concern that challenges both frameworks' assumptions about the availability of meaning in professional contexts.

Practical implications span career development practice, faculty curriculum design, and institutional strategy, with a central recommendation that institutions approach AI integration as a meaning-making challenge requiring vocational, ethical, and structural support rather than technology literacy alone. The findings suggest that AI in higher education is not merely a technology to be implemented or a policy to be managed, but a challenge that engages students' deepest commitments about who they are becoming and what kind of work will sustain a purposeful life.

Access Setting

Dissertation-Open Access

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