A qualitative-dominant convergent mixed methods study grounded in constructivist qualitative inquiry, examining how educators, school-system leaders, and adult university students make sense of pedagogical friction under conditions of generative AI.
Proposal defense passed August 13, 2026. Interview-question wording is being revised for committee review, followed by a planned three-educator flow rehearsal and an update to the IRB materials. IRB approval and formal study activity remain pending.
Explore the StudyTo investigate how K-12 educators, administrators, and system-level leaders understand, navigate, and respond to the friction-reducing affordances of generative AI in academic work.
Grounded in a constructivist qualitative inquiry, the study treats meaning as situated and co-constructed. The framework is a sensitizing and revisable lens, not a set of categories participants must confirm.
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?
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?
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?
Extending Walter Ong's framework to account for generative artificial intelligence.
Memory-based, communal, and situational. Knowledge is preserved through repetition and mnemonic devices.
Writing externalizes memory, enabling analytical detachment, individual authorship, and abstract reasoning.
Electronic broadcast media retrieve communal qualities, but operate through one-to-many literate infrastructure.
Social media platforms where humans create symbolic content, but opaque algorithms determine distribution and curation.
Algorithmic systems both curate and generate symbolic content, rendering human authorship optional at scale.
Three defining pressures that threaten the cognitive, dialogic, and authorial processes education depends on.
The work of synthesizing, interpreting, and making meaning can be offloaded to the system rather than occurring in the learner's mind.
Communicative environments flood with synthetic discourse. The origin of content becomes uncertain, and simulated interlocutors can replace genuine contestation.
Symbolic production can be severed from lived experience, personal commitment, intellectual risk, and accountability for claims.
When generative AI bypasses these processes, students can produce fluent and correct-looking work without engaging in the schema construction required for durable understanding. The artifact of learning is visible, but the learning process is uncertain.
Three learner-facing dimensions of productive resistance rest on an infrastructural layer that enables or constrains them.
Cognitive struggle. The learner-facing resistance encountered when wrestling with complex ideas, synthesizing information, and revising understanding.
Engagement with real audiences. The learner-facing struggle of translating internal thought into claims that can be questioned, revised, and defended.
Intellectual ownership. The learner-facing personal stake and authorial accountability that connect the learner to the work produced.
The institutional environment is not a neutral backdrop. Its policy, assessment, leadership, professional-learning, and governance conditions actively constrain or enable whether the other three dimensions can be sustained.
A qualitative-dominant convergent mixed methods design grounded in constructivism and organized around participant sensemaking.
The dissertation's controlling methods language is constructivist and qualitative. Interviews and other open-ended evidence form the core, while closed-ended survey items and national datasets provide supporting context. Adult university students are participants. AI-generated texts and agentic artifacts remain a distinct nonparticipant evidence source that retains at least one bounded, prespecified agentic-artifact task.
Open the Methodology Studio Memoing & AI-use protocol See how the literature review leads hereInterviews with classroom-facing educators, building-level administrators, district or system-level leaders, and adult university students; card-sort and document protocols; educator survey data; and secondary data compilation.
Thematic qualitative analysis through the Pedagogical Friction lens, with descriptive analysis, disaggregation, and cautious cross-tabulation of quantitative data.
Joint displays compare qualitative themes with quantitative patterns and document convergence, expansion, divergence, or silence across educator sensemaking, institutional conditions, learner retrospection, and structural indicators.
Selected prompts are administered as a distinct nonparticipant comparison source, including at least one bounded, prespecified agentic-artifact task. Systems, task count, or repeated runs may be scaled, but the agentic component remains. The artifacts are analyzed within the study but never treated as evidence about human experience or sensemaking.
The study is not built around matched pairs. It uses a clear evidence hierarchy that protects the qualitative core while preserving the supporting role of quantitative context.
A distinct nonparticipant comparison that retains a bounded agentic-artifact task and remains open to evidence that challenges the framework.
A digital resource hub expanding on the qualifying paper and dissertation proposal.
Current public hub for the Chapters 1-3 proposal and companion resources.
Scrollytelling walk through the argument, framework, equity boundary, and study design.
Intellectual-history resource for the proposal's conceptual lineage.
Catalog of public dissertation-related resources and reading paths.