A qualitative-dominant convergent mixed methods case study in Merriam's interpretive tradition, examining how K-12 educators and school-system leaders make sense of pedagogical friction under conditions of generative AI.
Explore the StudyTo investigate how K-12 educators and school-system leaders understand, navigate, and respond to the friction-reducing affordances of GenAI in academic work.
Grounded in a Merriam-centered case design and supported by mixed-methods pragmatism, the study treats educator and institutional sensemaking as the conceptually and temporally bounded case.
How do K–12 teachers understand and navigate the friction-reducing affordances of generative AI?
What institutional conditions enable or constrain friction-preserving pedagogy?
How can the Pedagogical Friction Framework inform AI policy development in K–12?
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 (radio/TV) retrieves communal qualities, but operates through one-to-many literate infrastructure.
Social media platforms where humans create symbolic content, but opaque algorithms determine distribution and curation.
Neural networks generate the symbolic content itself, making human authorship optional at scale. The source of content is no longer human.
Three defining characteristics that threaten the cognitive processes education depends on.
The externalization of cognitive labor moves from storage to generation. The work of synthesizing and making meaning is offloaded to the system rather than occurring in the learner's mind.
The flooding of communicative environments with synthetic discourse. The origin of content becomes uncertain, and simulated interlocutors replace genuine contestation.
Symbolic production is severed from lived experience. Text is produced without personal commitment, intellectual risk, or accountability for the claims advanced.
When generative AI is used to bypass these cognitive processes, the result is what Manu Kapur (2016) calls Unproductive Success. A student generates fluent, correct output without ever engaging in the schema construction required for durable understanding. The artifact of learning is visible, but the process of learning has been bypassed.
Four dimensions of resistance necessary for durable learning.
Cognitive struggle. The necessary resistance encountered when wrestling with complex ideas, synthesizing information, and engaging in deliberate practice.
Engagement with real audiences. The friction of translating internal thought into a structure that an external audience can understand.
Intellectual ownership. The personal stake and authorial stance that connects the learner's identity to the work produced.
Policy and institutional conditions that either enable or constrain the preservation of the other three dimensions of friction.
A qualitative-dominant convergent mixed methods case study in Merriam's interpretive tradition, bounded conceptually and temporally rather than by a single site.
The dissertation's controlling methods language is Merriam-centered: the case is K-12 educator and institutional sensemaking about pedagogical friction under conditions of generative AI. Stake and Yin remain useful comparison lenses, but quantitative, learner-perspective, and AI-artifact strands support the qualitative case rather than redefining it.
Open the Methodology Studio Memoing & AI-use protocol See how the literature review leads hereInterviews with classroom-facing educators, building administrators, district/system leaders, and a bounded learner-perspective strand; card-sort and document protocols; survey data; and secondary data compilation.
Descriptive and thematic qualitative analysis through the Pedagogical Friction lens, with descriptive analysis, disaggregation, and cautious cross-tabulation of quantitative data.
Joint displays comparing qualitative themes with quantitative patterns, documenting convergence, expansion, and divergence across educator sensemaking, institutional conditions, learner retrospection, and secondary indicators.
Administering selected prompts to AI platforms as a bounded artifact comparison between situated human pedagogical reasoning and decontextualized synthetic discourse.
Exploring both sides of the friction equation.
Testing theoretical claims about what AI-generated pedagogical reasoning lacks.
A digital resource hub expanding on the Qualifying Paper and Dissertation.
Foundational material expanding on the history of machine intelligence.
Dedicated summary of claims related to the pedagogy of friction.
Theoretical claims regarding medial ecology and Walter Ong.
Literature review material for the qualifying paper and dissertation.