Writing the Rules for Generative Machines: Tensions and Entanglements in Preservice Teachers' Classroom AI Policies

Synthesis: Nash and Burriss (2026) offer a qualitative instrumental case study of how 27 preservice English language arts teachers in the final year of a secondary teacher preparation program wrote their own classroom AI policies for 6-12 English classrooms. Framed by postdigital theory, sociocultural literacy theory, and a process view of writing, the study treats the 27 course policies as discursive texts that expose how early-career teachers negotiate competing discourses about generative AI in English instruction. Grounded-theory coding of the policies and their accompanying reflections produced five findings: policies (1) reflected ambivalence and ambiguity about AI-mediated ideation, (2) located the central work of thinking in text production and disallowed AI composition, (3) cautiously invited AI for checking, citing, and feedback, (4) largely failed to address reading, and (5) largely permitted AI for research and inquiry. The authors surface a persistent contradiction: participants unanimously sought to protect students as critical thinkers, yet allowed AI for the ideation and revision tasks they themselves described as thinking, while locating "real" thinking in final written products. A noticeable technodeterminism runs through the policies, with even skeptical teachers arguing that AI integration was unavoidable for students' futures. The study's key contribution is methodological — policy writing as a window into writing pedagogy, AI Literacy, and the shifting boundaries of what it means to read, think, and compose.

Key Findings

• Nearly every policy permitted some AI use, but almost always on teacher terms: 26 of the 27 policies allowed generative AI for at least some tasks, overwhelmingly restricted to teacher-specified tasks, times, and places; only one participant prohibited all classroom AI use, on ethical grounds tied to labor practices and copyright.
• Ideation and brainstorming were the most broadly permitted use — and the most ambiguous: 22 of 27 policies allowed students to generate ideas, brainstorm, or prewrite with AI, while only one explicitly operationalized its stated commitment to protecting thinking by banning AI for idea generation. Limits were rarely operationalized: Willa allowed AI "to get your thinking started" but declared "this is where the line should be drawn" without specifying where.
• The work of thinking was located in text production: 22 of 27 policies disallowed, or left unclear, the use of AI to compose sentences, paragraphs, or papers. Willa wrote that "the actual writing is the most crucial part of the assignment," reflecting a view of writing as intellectual performance assessment rather than a layered thinking process.
• Checking, citing, and feedback were cautiously invited: 16 participants permitted AI for grammar checking and proofreading (Andrea allowed Grammarly "in any context"; Theresa reserved "subjective" style feedback for the teacher), while 16 prohibited producing large AI-generated text. Two raised Privacy concerns about submitting student writing to models, and one disallowed AI citation generation entirely.
• Reading was largely unexamined: 22 of 27 policies did not mention reading at all. Of the five that did, most framed AI as a conversation partner for clarifying challenging texts; only two disallowed AI for reading, citing the productive struggle and comprehension work that summarization could short-circuit.
• Research and inquiry use was permitted with verification cautions: 11 participants allowed AI-supported searching with explicit demands that students assess credibility; two disallowed it. Several noted that AI-generated misinformation makes students responsible for submitted content, and one planned a lesson on validating AI output and recognizing bias.
• Technodeterminism shaped adoption against participants' own reservations: Even the most critical PSTs argued that AI would "not go away," that teaching about it was their duty, or that it mattered for students' future jobs — a stance that, the authors argue, both responds to and reproduces an AI-centric future.
• Policies were riddled with internal contradictions: Many policies simultaneously prohibited submitting AI text and held students responsible for AI-generated content they submitted, mirroring the conflicting messaging in broader school, district, and societal discourse rather than resolving it.

Study Design & Method

• Qualitative instrumental case study using grounded theory methods (Strauss and Corbin 1990), with open coding and recursive re-readings; the aim was recurring themes in a localized context, not formal theory.
• 27 preservice English language arts teachers in the final (capstone) year of a secondary English teacher preparation program at a mid-sized rural public university in the eastern United States, a state with no state-level AI guidance at the time.
• Frameworks: postdigital theory (technology as already woven into practice), sociocultural and material literacies theory, and composition research framing writing as a process that supports thinking.
• Data: the primary sources were participants' course AI policies and their written reflections on designing them; secondary sources included reading responses, annotated AI-use work, and course materials (slides, lesson plans, handouts, readings).
• Analysis: two researchers coded independently in synchronous sessions, built an Excel matrix of allowed, disallowed, and unclear uses, then reconciled into 21 categories across 27 data sets — 567 classifications with 98% agreement (555/567), with consensus discussion and member checking for the remainder.
• Limitations: a single two-class context, no empirical evidence from participants' actual teaching placements, and policies written speculatively for anticipated classrooms rather than enacted ones; the authors caution against generalization.

Implications for AI in Education

• Policy writing is a high-value teacher-education activity: Asking preservice teachers to author their own classroom AI policies surfaces their philosophies, beliefs, and curricular assumptions better than abstract discussion — it functions as rhetorical world-building about what writing is for.
• Teacher education must help PSTs deconstruct writing into its component parts: AI separates writing-as-product from writing-as-process. Programs should support teachers in asking where thinking, learning, and value actually live across brainstorming, drafting, revising, and editing — rather than locating thinking only in final text.
• Ambiguity is a policy defect, not a neutral choice: Contradictory rules (no AI text, but you are responsible for AI text you submit) leave students unable to comply; teachers need support translating beliefs into operational, unambiguous language and into AI Literacy instruction.
• Reading deserves parity with writing: With 22 of 27 policies silent on AI and reading, the field risks siloing critical evaluation and close-reading habits away from the digital spaces where most reading now happens — teachers should address AI-supported reading, summarization, and information literacy explicitly.
• Institutions must equip teachers to resist as well as adopt: The authors distinguish principled refusal from ignorance; districts and programs should provide the guidance, professional development, and policy infrastructure that let teachers decline specific AI uses without being framed as behind the times.
• Technodeterminism should be interrogated, not assumed: The obligation PSTs felt to integrate AI often contradicted their own pedagogical commitments; teacher educators should make the discourse of inevitability an explicit object of study and give teachers the Learner Agency to choose.

Connected Concepts

• Generative AI
• Educational AI Policy
• Professional Development
• Writing
• English Education (EAP / EFL / ESL)
• Critical Thinking
• Academic Integrity

Connected Articles

• Unpacking the Heterogeneity of Pre-service Teachers' ChatGPT Acceptance: A Latent Profile Analysis Across STEM and Non-STEM Disciplines — Preservice teachers' ChatGPT use and language-related pedagogical knowledge
• "Should I Tell My Teacher?" Student AI Disclosure Practices, Stigma, and Self-Regulated Learning in Higher Education — Student decisions about disclosing generative AI use to teachers
• Cheating or not cheating? Rethinking AI-giarism and academic integrity through secondary students' ethical reasoning — Rethinking plagiarism and integrity in secondary generative AI contexts
• From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership — AI-writing support and student ownership across writing stages
• How Instructors Regulate AI in College: Evidence from 31,000 Course Syllabi — Regulating generative AI through course syllabi and academic policy

Citation

Nash, B. L., & Burriss, S. K. (2026). Writing the rules for generative machines: Tensions and entanglements in preservice teachers' classroom AI policies. Reading Research Quarterly, 61(3), e70136.