Dissertation Overview

Current study focus.

This dissertation examines how classroom-facing educators, school-system leaders, and adult university students make sense of the friction-reducing affordances of generative AI in teaching and learning. 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 classroom-facing educators, building-level administrators, district or system-level leaders, and adult university students 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.

Participant perspectives include classroom-facing educators, building-level administrators, district or system-level leaders, and adult university students. Each group encounters teaching and learning under generative AI from a different instructional, supervisory, policy, governance, or learner position.

Supporting strands.

Adult university students remain participants; their retrospective accounts provide a supplementary 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.
The AI-generated text and agentic-artifact comparison remains distinct nonparticipant evidence, retains at least one bounded, prespecified agentic-artifact task, and is never treated as evidence about human experience.

The four dimensions of friction.

The Pedagogical Friction Framework names three learner-facing dimensions and one institutional condition. The fourth is not a peer of the other three. Infrastructural friction is what makes the learner-facing forms possible or impossible, which is why it is shown here as the ground the others stand on rather than as a fourth equivalent box.

Noetic friction.

Learner-facing

Preserves necessary cognitive work.

A generated summary can stand in for the encoding work that makes a reading retrievable weeks later. The artifact is complete; the cognitive trace behind it is thin.

Rhetorical friction.

Learner-facing

Preserves dialogue, audience, critique, and revision.

When a first draft arrives already polished, peer critique has nothing to grip and revision becomes cosmetic. The argument was never provisional enough to be argued with.

Existential friction.

Learner-facing

Preserves authorship, accountability, and the relationship between a person and a claim.

A student can competently present a position they never actually formed. The gap tends to surface only when a follow-up question arrives that the generated text did not anticipate.

Infrastructural friction.

System-level · enables or constrains the three above

The policy, leadership, professional-learning, assessment, and access conditions that make the learner-facing forms possible.

Where guidance is absent or unclear, teachers improvise one classroom at a time. The absence of policy is not neutral; it is itself a condition shaping what happens in the room.

The consequences above are illustrative readings of the framework, included to show how the dimensions differ in practice. They are not study findings. No participant data has been collected.

How the strands fit together.

The design is convergent (Creswell & Plano Clark, 2018). The qualitative and quantitative strands run at the same time, are analyzed separately and on their own terms, and are brought together only at interpretation through joint displays organized by the research questions.

QUAL · priority strand

Interviews across four participant perspectives.

Classroom-facing educators, building-level administrators, and district or system-level leaders, with adult university students contributing retrospective learner accounts of late high school as generative AI became publicly available.

quan · complementary strand

Exploratory educator survey and structural context.

An exploratory K-12 educator survey, with NCES School Pulse Panel and RAND American Educator Panel data supplying structural context rather than claims about this study’s participants.

point of integration

Joint displays organized by research question.

The qualitative strand carries priority and the survey strand is complementary, so neither is collapsed into the other during analysis. Integration happens at interpretation, where themes and survey patterns are set side by side and read as converging, diverging, or complementary evidence.

nonparticipant evidence

AI-generated text and agentic-artifact comparison.

Held deliberately apart from the participant strands and analyzed separately as nonparticipant artifacts. It retains at least one bounded, prespecified agentic-artifact task and is never treated as evidence about human experience.

Research questions.

RQ1: How do classroom-facing educators, building-level administrators, district or system-level leaders, and adult university students make sense of the ways generative AI changes the effort, dialogue, authorship, judgment, and participation involved in teaching and learning?

RQ2: How do educators and school-system leaders make sense of and respond to the policy, professional-learning, assessment, access, and governance conditions surrounding generative AI?

RQ3: What language, assessment expectations, instructional practices, professional supports, and leadership approaches do educators and school-system leaders identify as important for preserving forms of friction that support learning while reducing unnecessary barriers?

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.

Explore the public research ecosystem.

Immersive proposal companion. A visual, scrollytelling walk through the whole proposal: Ong's arc, tertiary algorithmicity and its three pressures, the friction framework, the equity boundary, and a live scenario card sort. The Friction Atlas. The Chapter Two literature review as seven interactive stations, with badges, saved progress, and a source codex that unlocks the reviewed literature as you work through it. Proposal Defense Studio. The workspace built for the August 2026 proposal defense, preserved as a record of the claims, methodology, committee questions, and intellectual history presented to the committee. Pedagogical Friction Studio. A polished intellectual-history experience for walking the conceptual argument, exploring its traditions, examining the framework, and browsing the mapped source library. Dissertation research ecosystem hub. A dashboard of public GitHub Pages sites supporting the current dissertation proposal and its completed qualifying-paper foundation. Tertiary algorithmicity companion. A focused conceptual companion on the paper's diagnostic vocabulary. Research media. Short public-facing videos used to explain the research problem and media-ecological framing.