Doctoral research in progress

Pedagogical friction in the age of generative AI

A qualitative-dominant convergent mixed methods study grounded in constructivist qualitative inquiry. The study examines how educators, leaders, and adult university students make sense of changes in effort, dialogue, authorship, judgment, participation, and institutional response.

Proposal stage · no participant research has begun

Interpretive orientation

Constructivism guides the study's questions and claims

The study begins from the premise that people do not simply report a single, context-free reality. They interpret ambiguous technological change through experience, role, institutional history, professional language, and interaction with others. Interviews therefore examine how participants notice cues, draw on available frames, build plausible accounts, and connect those accounts to action.

What this orientation does

It keeps participant meaning at the center, makes context part of the interpretation, and treats the researcher's analysis as reflexive and accountable rather than mechanically neutral.

What it does not do

It does not depend on a historical qualitative tradition label or claim that one account represents every school or learner.

Current research questions

One coherent set of questions across participant roles

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 participants identify as important for preserving forms of friction that support learning while reducing unnecessary barriers?

Evidence architecture

A qualitative core with quantitative support

Qualitative core

Semi-structured interviews
Card-sort explanations
Institutional and policy documents
Open-ended survey responses

Supporting quantitative evidence

Closed-ended survey items
NCES School Pulse Panel context
RAND educator-panel context
Descriptive patterns used to orient, compare, and complicate interpretation

Participants include classroom-facing educators, building administrators, district or system leaders, and adult university students. The design brings their accounts into conversation without collapsing meaningful differences among roles.

Qualitative analysis

A clear two-pass interpretive process

First pass: Code close to participant language, actions, tensions, and sensemaking cues.
Second pass: Develop and refine themes across accounts while preserving important differences and contradictions.
Reflexive checking: Use analytic memos, peer debriefing, and attention to discrepant accounts to test interpretations.
Mixed methods integration: Compare qualitative themes with survey and secondary-data patterns through concise joint displays.

AI-generated texts remain inside the study as a distinct nonparticipant comparison source. They may illuminate patterns in generated discourse, but they are not interviews, experiences, or substitutes for human participant accounts.

Current boundaries

What this public overview can responsibly claim

This is a proposal-stage research architecture. Participant recruitment, data collection, coding, thematic analysis, mixed methods integration, and findings have not occurred. Public pages describe the current plan and intellectual rationale, not completed research.

For the actively maintained research-question dashboard and connected proposal materials, visit the Dissertation Proposal Dashboard.

research visualization – beta