**The Human Experience of an AI-Entangled Pedagogy: A Post-Qualitative Inquiry
1. The Research Problem & Guiding Question
Current research on Generative AI in education largely focuses on instrumental concerns: student usage, institutional policy, and tool functionality. While vital, this work overlooks the central human drama of this technological shift: the lived, subjective, and often tense experience of the instructors who mediate it. This study addresses that gap. It moves beyond metrics of adoption to illuminate the phenomenological dimension of this disruption, particularly for educators who teach writing and textual creation not as a skill, but as a fundamental mode of inquiry and meaning-making.
The inquiry is therefore guided by one central question: How do higher education instructors experience and make sense of their evolving, entangled relationship with Generative AI in their daily pedagogical practice?
2. Core Concepts (Translated for Clarity)
To investigate this question requires a specific conceptual toolkit. The following three concepts are foundational:
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Pervasion:
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This concept is used to name the silent, systemic, and non-voluntary invasion of AI into our personal, professional, and pedagogical lives. Unlike previous tools that could be adopted or set aside, GenAI cannot be escaped. It fuses with our intellectual presence by sequencing language intended to align with our thoughts, which in turn can seed and shape those thoughts. It is the ultimate cognitive prosthetic, blurring the line between user and tool, much like a driver and a car become a single functional unit. Even if an instructor bans AI, they and their students are still navigating a world saturated with it, making confrontation with its effects inevitable.
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Boundary-Drawing (Agential Cut):
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This refers to the constant, practical, and often invisible work instructors do to manage AI. It is the drawing and redrawing of lines that determine what is acceptable, what is human, what is machine, and how the two can legitimately collaborate. These are not abstract rules, but concrete pedagogical decisions enacted in syllabi, assignment prompts, and feedback.
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Cyborg Pedagogy & The Pedagogy of Cyborgification: cyborg - prp central figuration
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This study makes a crucial distinction between two related ideas. Cyborg Pedagogy names the state of the instructor-AI hybrid—the new, entangled practitioner whose judgment and practice emerge from a coupling of human and machine cognition. In contrast, a Pedagogy of Cyborgification is the process of learning how to become a “good” cyborg. It is a pedagogy focused on cultivating the skills of ethical awareness, critical discernment, and reflective practice necessary to navigate this hybridity responsibly. This process modifies the intellectual and practical “body” of both teacher and student.
3. The Method: A Practical Workflow
The study will follow a phenomenologically-oriented, narrative-practice approach with 4-6 instructors over 6-8 weeks. The workflow is designed to capture experience as it unfolds:
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Collect: Gather rich, in-the-moment data through three methods: (1) brief, solicited micro-diaries capturing day-to-day encounters with AI; (2) three short critical incident narratives detailing moments of awe, tension, or practice change; and (3) two artifact-anchored conversations focused on the tangible materials of teaching (syllabi, prompts, rubrics).
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Analyze: Analysis is not a search for pre-existing themes, but a process of tracing movement and entanglement. It proceeds in three passes: First, a diffractive reading of the diaries and incidents to trace how instructors’ experiences, affects, and practices reconfigure over time. Second, a material-discursive tracing of the artifacts (syllabi, prompts) to analyze what these documents do and how they enact pedagogical realities. Finally, a synthesis that clusters these patterns by the type of boundary they perform—instrumental, collaborative, or cyborgian—to build a rich map of emerging practice.
4. Why This Methodology? (Postqualitative, Posthumanist & New Materialist)
The three pillars
""A traditional qualitative study would be excellent for capturing a snapshot of what instructors think about AI. But my project is built on the premise that the phenomenon itself—this deep entanglement with AI—is not static. It’s a dynamic process of becoming.
Therefore, a post-qualitative approach is necessary because it allows me to move beyond capturing opinions and instead trace the movement of practice over time. It lets me study how the instructor, the AI, the syllabus, and the institutional policies are all acting together to produce new pedagogical realities. The method is designed to match the fluid, entangled nature of the problem itself.""
A traditional qualitative study might use interviews to create a static snapshot of what instructors think about AI, coding their responses into themes like “anxiety” or “excitement.” This approach is insufficient for the phenomenon at hand. It reduces the complexity of lived experience to simple emotional labels and fails to capture the dynamic, unfolding nature of this entanglement.
In contrast, this study’s post-qualitative, posthumanist, and new materialist orientation is a deliberate choice to do something different.
- It emphasizes movement over stasis. By collecting data longitudinally through diaries and evolving artifacts, the study can trace how practices, beliefs, and feelings shift from week to week, rather than capturing a single, fixed opinion.
- It preserves the complexity of affect over emotion. It seeks to understand the visceral, embodied, and often nameless tensions of teaching with AI—the “felt sense” of a situation—rather than reducing these experiences to tidy psychological categories like “the instructor is anxious because…”
- It studies entanglement over interaction. It moves beyond a simple subject-object view (instructor uses tool) to see how agency is distributed. The AI, the syllabus, the institutional policy, and the instructor are not separate entities; they are an entangled apparatus that co-produces what happens in the classroom.
This methodology is therefore a necessary choice to attune the research to a reality that is fluid, complex, and more-than-human.
| A Traditional Qualitative Study Would… | This Post-Qualitative Study Will… |
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| Ask: “What do instructors think about AI?” | Ask: “What realities are enacted when instructors and AI get entangled?” |
| See the Instructor as: A stable, autonomous person who uses a tool. | See the Instructor as: A hybrid, “cyborg” practitioner who is co-produced with the tool. |
| Treat Data as: A reflection of a pre-existing reality (e.g., an interview transcript reveals “anxiety”). | Treat Data as: A performative trace of a reality being made (e.g., a diary entry shows affect in motion). |
| The Goal is to: Find and name static themes (e.g., “fear,” “excitement,” “resistance”). | The Goal is to: Trace dynamic processes and reconfigurations (e.g., how a practice moves from policing to collaboration). |
| The Metaphor is: The researcher as a neutral mirror, reflecting what is there. | The Metaphor is: The researcher as a cartographer, mapping the new, shifting landscape as it emerges. |
5. Expected Contribution
This research will produce two key outcomes: first, a set of thick, grounded portraits of what it feels like to teach in this moment of profound technological disruption. Second, and more practically, it will deliver a set of transferable principles and concrete examples for assignment design, feedback practices, and policy language that support accountable, ethical, and pedagogically rich forms of human-AI collaboration.