The theory is established. The proposal asks how it holds up in practice.
The completed qualifying paper developed the conceptual argument: under tertiary algorithmicity, generative systems can make polished academic performance easier to produce while displacing the noetic, rhetorical, and existential work through which durable learning develops. The current dissertation proposal turns that argument toward an empirical question. It examines how K-12 educators and school-system actors understand, navigate, and respond to those conditions across classrooms, buildings, and systems.
Completed Qualifying Paper
Provides the theoretical apparatus: algorithmic secondary orality, tertiary algorithmicity, three media-ecological pressures, and the Pedagogical Friction Framework. It is the conceptual foundation for the study, not a report of empirical findings.
Current Chapters 1–3 Proposal
Defines the problem, synthesizes the literature, bounds the case, states the research questions, and specifies how evidence will be collected, analyzed, integrated, and protected.
Future Dissertation Study
After committee and IRB approval, the study will examine educator and institutional sensemaking. Findings and implications belong to future Chapters 4 and 5 after analysis is complete.
Chapters 1–3 Proposal Architecture
- Chapter One: Establishes the problem of practice, purpose, bounded case, unit of analysis, research questions, significance, and proposal-stage scope.
- Chapter Two: Connects media ecology, learning science, equity, AI governance, and K-12 research to the framework and empirical gap.
- Chapter Three: Specifies a Merriam-aligned, qualitative-dominant convergent mixed methods case study and the safeguards governing collection, analysis, integration, and interpretation.
Problem of Practice
K-12 schools are adopting generative AI while still lacking a precise empirical and conceptual language for distinguishing forms of ease that support access from forms of ease that bypass learning. The study begins from a pattern visible in practice: student work can become cleaner and more fluent while student understanding becomes less secure.
Purpose
The study examines how educators, administrators, and system-level leaders make sense of pedagogical friction, and what policy, assessment, professional learning, leadership, and governance conditions enable or constrain friction-preserving pedagogy.
Case and Unit
The case is K-12 educator and institutional sensemaking about pedagogical friction under conditions of generative AI. The primary unit of analysis is not AI use itself, but judgments about whether friction should be preserved, reduced, or redesigned.
Research Design
The proposal uses a qualitative-dominant convergent mixed methods design within a Merriam-aligned interpretive case study. Qualitative and quantitative evidence are analyzed independently and then integrated through joint displays, narrative synthesis, and proportionate meta-inferences.
Framework
The Pedagogical Friction Framework responds to tertiary algorithmicity by naming three learner-facing dimensions of productive resistance, noetic, rhetorical, and existential friction, and treating infrastructural friction as the institutional condition that enables or constrains them. Whether institutional conditions make classroom-level friction sustainable is the central relationship the study examines.
Primary Perspectives
- Classroom-facing educators
- Building-level administrators
- District or system-level leaders
Evidence Within the Bounded Case
- Role-based educator and leader interviews
- Card sorts that make professional judgment visible
- Institutional and public documents
- Branched educator survey data
Contextual and Supplementary Evidence
- Retrospective university student accounts (supporting learner context)
- NCES School Pulse Panel and RAND American Educator Panel (structural context)
- AI-generated artifact comparison, outside the bounded case and reported only as an appendix-level supplement
Equity Guardrail
The framework distinguishes productive friction, which supports learning, from exclusionary friction, which blocks access or participation without educational benefit.
Primary Research Questions
The dissertation web ecosystem, indexed.
This dashboard gathers the live public pages that support the current dissertation proposal and its completed qualifying-paper foundation: the theoretical companions for tertiary algorithmicity and pedagogical friction, the literature review, the mixed methods proposal, the planning tools, the participant-facing instrument suite, and the prospective outcomes-and-action artifacts that model what responsible interpretation and community return could look like after analysis. The catalog should be read through the current Chapters 1-3 frame above: a K-12 study of educator and institutional sensemaking about when generative AI supports learning and when it bypasses the work learning requires.
Open ecosystem
Browse every public dissertation-related resource, or choose a path to narrow the catalog around a specific reader need.
Not a file cabinet. A research constellation.
Researcher Note
Use this page as the public-facing map of the dissertation web ecosystem. It is safe for public links, descriptions, review navigation, and route selection. It is not a place for participant uploads, interview transcripts, audio files, coded data, or anything private.
Suggested Reading Path
- Start with the current Chapters 1–3 study frame to understand the proposed empirical study.
- Return to the completed qualifying paper and conceptual companions for the theory that grounds the proposal.
- Move through the literature review, methodology alignment, mixed methods plan, and measures studio to inspect the study architecture.
- Use the instrument suite only for reviewed, IRB-aligned participant workflows.
- Explore the potential-outcomes artifacts to rehearse responsible interpretation, community return, interventions, and evidence-proportionate action before findings exist.