Document Type
Article
Publication Title
World Journal of Arts, Education and Literature
Abstract
This article advances a field-ready framework that reconceives first-year composition as AI-native coding, translating a complete “design kit” into scholarly method, evaluative protocol, and curriculum architecture. Background: Contemporary composition pedagogy emphasizes process, genre awareness, and collaborative revision; meanwhile, modern software practice operationalizes iteration through version control, test-driven development, and continuous integration. The uploaded kit demonstrates that these cultures are isomorphic: writing stages align with SDLC phases, and automated pipelines can lint prose, execute argument “tests,” and publish artifacts with auditable histories. Approach: The study systematizes that kit into (1) a conceptual map that recasts authorship as orchestration and verification, (2) a pipeline specification that integrates rhetorical linters, claim-evidence checks, retrieval-grounded fact audits, and CI dashboards, and (3) an assessment regime that grades specification quality, revision discipline, and process transparency alongside argument strength and source integration. Significance of results: The framework yields inspectable process evidence that reduces adjudication ambiguity, raises floor quality on conventions through automation, and reallocates instructor attention to higher-order reasoning; it further proposes a mixed-methods research program that couples CI telemetry with blinded ratings to estimate effects on argument adequacy, equity for multilingual writers via audit trails, and transfer across disciplines. By treating “voice” as measurable style alignment under constraints and “authorship” as documented governance over generative systems, the model offers a reproducible answer to integrity, workload, and scalability in an AI-saturated academy. The contribution is a discipline-legible, automation-forward blueprint that programs can adopt in enhanced, driven, or autonomous variants without requiring coding prerequisites, supported by ready-to-deploy rubrics, YAML exemplars, and policy templates.
Publication Date
11-2025
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Plate, Daniel and Hutson, James, "Composition Pedagogy as AI‑Native Coding: From Design Kit to Scholarly Framework" (2025). Faculty Scholarship. 784.
https://digitalcommons.lindenwood.edu/faculty-research-papers/784