Fall 2026 Doctoral Colloquium · November 7, 2026
Pedagogical Frictionin the Age of Generative AI and Tertiary Algorithmicity
A qualitative-dominant convergent mixed methods study
Approved as exempt by the NLU IRB, protocol ER01884. This is research in progress. No findings are reported.
Research topic · The problem
Generative AI can produce the outputs of learning without the learning
What can now be generated
Fluent text, code, images, and analyses
Outputs that may be difficult to distinguish from human-authored work
What learning involves
Effort, dialogue, authorship, judgment, and participation
The process the product no longer shows
“The problem is that schools lack a sufficiently precise empirical and conceptual language for judging which forms of ease support access by removing exclusionary friction and which forms of ease bypass learning by removing productive friction.”
Dissertation proposal, Chapter One (defended August 13, 2026)
Purpose
Four vantage points on the same change
Click a group
“The purpose of this qualitative-dominant convergent mixed methods study is to investigate how classroom-facing educators, building-level administrators, and district or system-level leaders understand and navigate the effects of generative AI on teaching, learning, authorship, assessment, and institutional decision making, while adult university students contribute retrospective learner perspectives on cognitive engagement, authorship, and disclosure norms.”
Classroom-facing educators describe how AI changes the work students do and how they judge it. They also complete a short scenario card sort.
Building-level administrators describe the school-level guidance, professional learning, and assessment expectations that shape classroom choices.
District or system-level leaders describe the policy, governance, and institutional decisions that enable or constrain classroom practice.
Adult university students contribute “retrospective learner perspectives on cognitive engagement, authorship, and disclosure norms.” They also complete the card sort.
Dissertation proposal, Chapter One
Research questions
Three questions move from experience to conditions to supports
Click a question to see its evidence
RQ1 draws on
Sources. Interviews across all four groups; the scenario card sort for classroom-facing educators and adult university students; educator survey, institutional documents, and selected national evidence as context.
Claim boundary. Situated interpretations of how participants understand these changes. No direct measurement of student learning outcomes.
RQ2 draws on
Sources. Educator and leader interviews; card-sort explanations where relevant; document analysis; the educator survey; selected NCES and RAND evidence.
Claim boundary. Conditions that enable or constrain friction-preserving pedagogy, not causal effects or representative national estimates.
RQ3 draws on
Sources. Integrated RQ1 and RQ2 evidence. No additional participant group or instrument.
Analysis. Cross-role synthesis, joint displays, narrative weaving, and meta-inferences that keep convergence, divergence, expansion, and silence visible.
Theoretical framework · Pedagogical friction
Some friction is worth keeping, and some is worth removing
“the intentional preservation of educationally productive resistance necessary for durable learning under conditions where frictionless automation has become increasingly available”
Productive friction
Difficulty that builds capacity
Supports the intended learning, so it is worth preserving.
Exclusionary friction
Difficulty that blocks access
Creates barriers unrelated to the intended learning, so it is worth removing.
The equity boundary. The study asks whose learning a given difficulty supports and whose participation it obstructs (Annamma et al., 2013; Dolmage, 2017). The framework is not a call to ban AI tools or make school harder. Choose an example.
The same tool use can reduce both kinds of friction at once.
An exclusionary barrier: writing in an additional language
Language choice, voice, reasoning, and authorship
AI may work as accessibility support rather than as a cognitive shortcut.
Instructional design, context, and human oversight
Recoding legitimate accommodation as bypass
Definition, terms, and examples: dissertation proposal, Chapter Two
Theoretical framework · Structure
Three learner-facing forms rest on one institutional condition
Click a form
Each learner-facing form answers a pressure that AI creates. Choose a form to see the pressure and the practices that preserve it.
When AI substitutes for cognitive work that supports understanding
Generation before solution, retrieval practice, annotated reading, slow writing, argument mapping, worked-example fading, and structured reflection before any AI use
When generated language weakens genuine exchange or obscures the source of a claim
Socratic seminars, peer review, oral explanation, conferences, debate, critique protocols, and authentic audiences
When a polished artifact becomes separated from a learner’s willingness or ability to own and defend it
Oral defenses, reflective authorship statements, process memos, place-based inquiry, and assignments that connect claims to a student’s own reasoning
Individual teachers cannot preserve the other three alone if grades, pacing guides, device environments, parent expectations, and district policy reward frictionless completion
AI policy, assessment expectations, professional development priorities, privacy governance, approved-tool ecosystems, leadership language, and schedules that permit slowing learning when it is educationally necessary
Dissertation proposal, Chapters One and Two
Theoretical framework · Media ecology
Why “tertiary algorithmicity”
Media change how people think and communicate.
Algorithms select, organize, and give form to cultural material.
Algorithmic systems both curate and generate symbolic content, making human authorship optional at scale.
“Humans are not absent, but their role may shift from primary producer to …”
Dissertation proposal, Chapter Two
Methodology · Grounded in constructivist qualitative inquiry
Qualitative-dominant convergent mixed methods (QUAL + quan)
Click a part of the design
Creswell & Plano Clark (2018); Braun & Clarke (2006, 2021); Saldaña (2021). Analysis details: defense methods matrix
Methodology · Participants and instruments, as approved
Who takes part and what they do
Interviews · up to 20
Anonymous K–12 educator survey · about 10 to 12 minutes
Where I am in the process
From proposal to data collection
Dates after November 7 are plans. The consents set the study period through February 2027.
Anticipated contributions · No findings yet
What the study may contribute
“examining whether tertiary algorithmicity and the Pedagogical Friction Framework are useful in empirical practice”
“may help educators distinguish outputs that look like learning from processes that produce learning”
“shifts attention from academic integrity alone to learning design”
AI governance becomes a curricular and institutional question about the conditions that support human learning.
Dissertation proposal, Chapter One
Reflections and next steps
What the process has taught me so far
Narrowing the design
Before the defense, I removed the case-study frame. The study now reads four role-based perspectives side by side, which fits the questions better.
Ethics shapes recruitment
Invitations are open calls only. People contact me first, and screening follows the approved scripts word for word.
AI in my own process
AI tools help me organize and check my work. Coding, interpretation, and claims stay with me, and identifiable data never goes into them.
Thank you
Questions and conversation
Which friction in your own setting is worth protecting, and which is worth removing?
NLU IRB protocol ER01884 · Research in progress · References follow
References
Annamma, S. A., Connor, D., & Ferri, B. (2013). Dis/ability critical race studies (DisCrit): Theorizing at the intersections of race and dis/ability. Race Ethnicity and Education, 16(1), 1–31. https://doi.org/10.1080/13613324.2012.730511
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
Dolmage, J. T. (2017). Academic ableism: Disability and higher education. University of Michigan Press.
Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457
Ong, W. J. (2002). Orality and literacy: The technologizing of the word (2nd ed.). Routledge. (Original work published 1982)
Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). SAGE.
Stalder, F. (2018). The digital condition (V. A. Pakis, Trans.). Polity Press. (Original work published 2016)
Entries copied from the defended proposal’s reference list