GenAI bypasses the interpretive, cognitive, and rhetorical labor necessary for learning. The central educational problem is "unproductive success"—students producing correct academic artifacts without experiencing the cognitive struggle that restructures consciousness and builds durable schemas.
Defense Preparation Sandbox
Interactive practice tools, expected committee questions, and mixed methods design simulator.
🛡️ Expected Defense Questions
Use these active recall flashcards to prepare for committee-specific questioning (Dr. Terri Jo Smith & Dr. Ruben Puentedura).
How is Generative AI a curriculum and pedagogy issue, rather than just an academic integrity/cheating issue?
💡 Click card to flip and reveal answer
Core Argument: Unproductive Success & Cognitive Labor Bypass
"The central educational problem posed by generative AI is the bypassing of interpretive labor, intellectual accountability, and the developmental conditions for learning rather than academic integrity." (Chapter 1, Statement of the Problem)
🔬 Mixed Methods Joint Display Simulator
Click a Research Question to trace how its qualitative and quantitative data strands integrate into a visual joint display.
Research Question 1 (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?
QUAL STRAND (Evidence)
• Practitioner Interviews
• Card Sort protocol
• Transcripts coded for noetic, rhetorical, and existential friction.
• Card Sort protocol
• Transcripts coded for noetic, rhetorical, and existential friction.
quan STRAND (Context)
• RAND American Educator Panel surveys
• Original educator survey instrument (frequency of AI use, perceived rigor).
• Original educator survey instrument (frequency of AI use, perceived rigor).
Mixed Methods Integration
Joint display presenting side-by-side narrative findings matching survey frequencies with interview accounts of cognitive delegation vs. preservation.
🎯 Proposal Milestones
📖 Dissertation Proposal Core Structure
Select a chapter to expand its structure, core arguments, and methodological components.
Chapter 1: The Research Foundation
Statement of the Problem
School districts are rushing to adopt policy, purchase tools, or direct acceptable use for Generative AI, but their approaches are structured around a false framing. By viewing AI primarily as an academic integrity/cheating crisis, they miss the deeper threat: how friction-reducing technologies bypass the cognitive and interpretive struggles necessary to restructure human consciousness.
Purpose of the Study
This qualitative-dominant mixed methods study investigates how educators, school-system leaders, and adult university students make sense of pedagogical friction under generative AI.
Significance & Researcher Positionality
As a K-12 instructional technology administrator, the researcher brings a critical, technoskeptical lens that resists technological determinism. This research shifts the debate from a simple binary of "ban vs. embrace" to one of intentional pedagogical design.
Significance: It establishes a clear warrant for the preservation of cognitive struggle (the "generation effect" and "desirable difficulties") in instruction when automated writing assistants make production effortless.
Chapter 2: Review of the Literature & Theoretical Framework
Ong's Media Ecology Stepper
My theoretical framework extends Walter Ong's developmental account of communication media. Select each stage to inspect the transition:
1
Primary Orality
Communal, situational, formulaic memory.
2
Literacy
Analytical detachment, critical interiority.
3
Secondary Orality
Broadcast media retrieves oral performance.
4
Algorithmic Secondary
Human-made content; algorithmic circulation.
5
Tertiary Algorithmicity
Opaque machines generate and curate.
Current Focus: Algorithmic Secondary Orality
A transitional stage where human-created symbolic content continues, but algorithms determine what reaches which consciousness. Editorial judgment is optimized for engagement rather than truth or shared civic space.
A transitional stage where human-created symbolic content continues, but algorithms determine what reaches which consciousness. Editorial judgment is optimized for engagement rather than truth or shared civic space.
🧠 Noetic Displacement
Cognitive and interpretive labor is externalized to external systems, leading to correct outputs without human schema formation.
🗣️ Rhetorical Saturation
Synthetic dialogue floods the environment, generating responsive text without human dialogic responsibility or mutual risk.
⚖️ Existential Abstraction
Claims are severed from accountable claimants, allowing information to circulate without anyone backing the claims.
Chapter 3: Constructivist Qualitative Methodology
Constructivist Qualitative-Dominant Design
The proposed research is organized as a qualitative-dominant convergent mixed methods study grounded in constructivist qualitative inquiry. Classroom-facing educators, building-level administrators, district or system-level leaders, and adult university students contribute situated participant perspectives.
Constructivism treats meaning as situated and co-constructed. Sensemaking directs attention to how participants notice cues in ambiguous conditions, interpret them through available frames, form plausible accounts, and act on those accounts.
Data Sources Overview
- Proposed qualitative core: Semi-structured interviews, card-sort explanations, institutional documents, and open-ended survey responses.
- Adult university students: Participant accounts providing a learner perspective within RQ1 and the qualitative analysis.
- quan (Educator Survey): Exploratory survey of K-12 educators' perceived rigor vs. AI convenience.
- quan (Secondary Datasets): Analyzing NCES School Pulse Panel data and RAND American Educator Panels on national K-12 AI integration for structural context.
- AI-generated text comparison: A distinct nonparticipant evidence source within the study, analyzed separately and never treated as human experience.
Methodology Flowchart
Phase 1: Concurrent Evidence Collection
• Collect evidence according to the research questions and each source's analytic role.
• Collect QUAL interviews & card sorts alongside quan surveys & NCES datasets.
Phase 2: Inductive Interpretative Analysis
• Code QUAL transcripts for meaning-making, focusing on noetic, rhetorical, and existential dimensions.
• Run appropriate quan descriptive and conditional comparative analyses.
Phase 3: Mixed Methods Integration
• Construct side-by-side Joint Displays tracing each RQ, matching qualitative descriptions with quantitative frequencies.
• Collect evidence according to the research questions and each source's analytic role.
• Collect QUAL interviews & card sorts alongside quan surveys & NCES datasets.
Phase 2: Inductive Interpretative Analysis
• Code QUAL transcripts for meaning-making, focusing on noetic, rhetorical, and existential dimensions.
• Run appropriate quan descriptive and conditional comparative analyses.
Phase 3: Mixed Methods Integration
• Construct side-by-side Joint Displays tracing each RQ, matching qualitative descriptions with quantitative frequencies.
⏳ Curated Proposal Intellectual History
The same proposal-defense sequence used in the Proposal Defense Studio, connecting media ecology, learning science, algorithm studies, AI education, and methods.