Core Problem & Framework
The Problem: Generative AI can make schoolwork appear successful while bypassing interpretive labor, authorial ownership, and durable learning processes. Output looks like learning.
The Framework: Pedagogical friction distinguishes productive friction (which supports interpretation, authorship, and judgment) from exclusionary friction (which blocks access). The study examines three learner-facing dimensions: Noetic, Rhetorical, and Existential. Infrastructural friction is the institutional conditioning layer that can sustain or weaken the other three.
Research Questions & Methods
| Research Question | Participants & Instruments | Constructs & Analysis |
|---|---|---|
| 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? | All four participant groups. Interviews, card-sort explanations, documents, and open-ended survey responses. | Noetic, rhetorical, existential friction. Thematic analysis and card-sort reasoning. |
| 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? | Educators and school-system leaders. Interviews, documents, and survey responses. | Infrastructural friction. Thematic comparison and document analysis. |
| RQ3: 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? | Educator and school-system leader evidence with supporting survey and national context. | Cross-role synthesis, joint-display integration, meta-inference development. |
Primary Claims
1. Polished output is not reliable evidence of learning.
Generative AI text mimics cognitive processing; educators must find new ways to verify reasoning.
2. Pedagogical friction is a design and governance concept.
Not all difficulty is productive; schools must govern which forms of friction to preserve and which to reduce.
3. Tertiary algorithmicity alters learning conditions.
Algorithmic generation shifts meaning-making from human interpretation to machine-shaped defaults.
Methodological Boundaries
- Scope: The study examines sensemaking among educators, school-system leaders, and adult university students. It does not measure direct student learning outcomes.
- Generalizability: As an interpretive qualitative study, the findings may support reasoned transfer to similar contexts, but they do not provide universal statistical generalizability.
- Integrity vs. Learning: The study deliberately pivots from academic misconduct policies to focus on the preservation of learning processes.