Current study focus.
This dissertation examines how K-12 educators and school-system leaders understand, navigate, and respond to the friction-reducing affordances of generative AI in academic work. The central issue is not simply whether students use AI, but what happens when polished language, summaries, arguments, explanations, and performances can be produced without the interpretive and authorial labor those outputs have traditionally signaled.
The study begins from a practical pattern now visible in schools: student work can become cleaner and more fluent while student understanding becomes less secure. That problem cannot be reduced to cheating alone. It raises a learning-science and media-ecological question about the conditions under which students develop durable understanding.
Problem and purpose.
K-12 educators are being asked to respond to generative AI without an empirically grounded framework for distinguishing AI uses that support learning from AI uses that bypass the cognitive and social conditions learning requires. Current guidance often emphasizes academic integrity, acceptable use, privacy, bias, and AI literacy. Those issues matter, but they do not fully address the learning processes at stake when students outsource drafting, synthesis, explanation, translation, revision, or argumentation to generative systems.
The purpose of the study is to investigate how educators, administrators, and system-level leaders make sense of pedagogical friction under conditions of generative AI, and what policy, assessment, professional learning, leadership, and governance conditions enable or constrain friction-preserving pedagogy.
Constructivist qualitative inquiry.
The study uses a qualitative-dominant convergent mixed methods design grounded in constructivism. Participant meaning is interpreted as situated and co-constructed, with the Pedagogical Friction Framework serving as a sensitizing and revisable lens rather than a container for the inquiry.
Participant perspectives.
The primary study centers classroom-facing educators, building-level administrators, and district or system-level leaders because each group encounters AI through different instructional, supervisory, policy, and governance responsibilities.
Supporting strands.
- Retrospective university student accounts provide contextual learner perspective.
- An exploratory K-12 educator survey provides supporting quantitative evidence.
- NCES School Pulse Panel and RAND American Educator Panel data provide structural context.
- An AI-generated text comparison remains a distinct, nonparticipant evidence source within the study and is never treated as evidence about human experience.
Research questions.
Why it matters.
The study shifts attention from academic integrity alone toward learning design. A friction-centered approach asks whether noetic, rhetorical, existential, and infrastructural conditions for learning are protected; whether supports remove exclusionary barriers without eliminating productive struggle; and whether teachers have institutional permission to redesign assessment around visible thinking.
This public overview is explanatory only. It should not be used to collect participant responses, store identifiable research data, or publish private dissertation materials. Recruitment, consent, participant data, and analysis records must remain private and IRB-governed.