Dissertation research

Pedagogical friction in the age of generative AI

This hub connects the current proposal architecture, methodological rationale, evidence base, and committee-facing preparation materials for a qualitative-dominant convergent mixed methods study.

Proposal defense passed August 13, 2026 · IRB pending

Current study architecture

Constructivist qualitative inquiry drives the study's attention to sensemaking. Interviews, card-sort explanations, documents, and open-ended survey responses form the qualitative core. Closed-ended survey items and NCES and RAND sources provide supporting quantitative context.

Start here

The canonical public overview of the study's purpose, framework, research questions, participants, and methods.

Open the dissertation overview

Concise methods overview

A public explanation of constructivism, sensemaking, the two-pass qualitative analysis, and the role of AI-generated texts.

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Current research questions

RQ1How 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?

RQ2How do educators and school-system leaders make sense of and respond to the policy, professional-learning, assessment, access, and governance conditions surrounding generative AI?

RQ3What 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?

Connected proposal materials

Proposal overview

A concise map of the problem, framework, design, and contribution.

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Methodology alignment

Constructivist assumptions, sensemaking, evidence roles, and qualitative analysis.

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Proposal defense preparation

Committee-facing explanations, questions, and design rationale.

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Pedagogical Friction Framework

The current conceptual framework and its implications for teaching and leadership.

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Literature bridge

The transition from the qualifying paper into the dissertation's constructivist inquiry.

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K-12 AI evidence arc

Secondary-data context and reproducible evidence used to orient the study.

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Current methodological alignment

The current proposal uses a direct constructivist qualitative inquiry. It retains the instruments, mixed methods design, participant groups, secondary evidence, and AI-generated text comparison while making the qualitative core clearer and easier to follow.

Research boundary: AI-generated texts are a distinct nonparticipant comparison source within the study. They do not have experience, are not interviewed participants, and are not evidence of human sensemaking.

Pre-IRB research guardrail

IRB approval remains pending. Formal recruitment and main-study data collection have not begun, and no main-study findings are reported. Older public prototypes may remain available as part of the project's intellectual history. Where public wording differs, the submitted Chapters 1–3 control; the dissertation overview is the canonical public summary.

The qualifying paper and older planning artifacts document the path into the dissertation. They should not be read as the current Chapter 3 design.

Dissertation Research