Composition Pedagogy as AI‑Native Coding: From Design Kit to Scholarly Framework
Keywords:
AI-native composition, continuous integration for writing, rhetorical linting, test-driven writing, audit-trail assessmentAbstract
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.
References
1. Association of American Colleges & Universities. (2018). VALUE rubrics. AAC&U Press.
2. Barad, K. (2007). Meeting the universe halfway: Quantum physics and the entanglement of matter and meaning. Duke University Press.
3. Bates, T. (2022). Teaching in a digital age: Guidelines for designing teaching and learning. Tony Bates Associates.
4. Berenguel, J. (2024). Test-driven writing (or test-driven documentation). Mostly Maths Blog.
5. Blum, S. D. (2020). Ungrading: Why rating students undermines learning (and what to do instead). West Virginia University Press.
6. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
7. Cummings, R. E., Monroe, S. M., & Watkins, M. (2024). Generative AI in first-year writing: An early analysis of affordances, limitations, and a framework for the future. Computers and Composition, 71, 102827. https://doi.org/10.1016/j.compcom.2024.102827
8. Devitt, A. J. (2004). Writing genres. Southern Illinois University Press.
9. Dhuliawala, S., Saparov, A., Ng, J., Durmus, E., Santurkar, S., Christoffersen, P. J., Liang, P., Hashimoto, T. B., & Clark, J. (2024). Chain-of-Verification reduces hallucination in large language models. Findings of the Association for Computational Linguistics: ACL 2024, 212–226. https://doi.org/10.18653/v1/2024.findings-acl.212
10. Drucker, J. (2020). Visualization and interpretation: Humanistic approaches to display. MIT Press.
11. Emig, J. (1977). Writing as a mode of learning. College Composition and Communication, 28(2), 122–128. https://doi.org/10.2307/356095
12. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. E. J. (2024). A SWOT analysis of ChatGPT: Implications for educational practice. Innovations in Education and Teaching International, 61(2), 132–143. https://doi.org/10.1080/14703297.2023.2195846
13. Flower, L., & Hayes, J. R. (1981). A cognitive process theory of writing. College Composition and Communication, 32(4), 365–387. https://doi.org/10.2307/356600
14. Fütterer, T., Krell, M., & Scheiter, K. (2023). ChatGPT in education: Global reactions to AI innovations. Scientific Reports, 13, 17136. https://doi.org/10.1038/s41598-023-42227-6
15. Goode, J., Friend, T., & Petrovic, M. (2023). Building faculty capacity for AI-integrated pedagogy: From technical skill to epistemic agility. Computers & Education, 206, 104872. https://doi.org/10.1016/j.compedu.2023.104872
16. Huot, B. (2002). Toward a new theory of writing assessment. Utah State University Press.
17. Journet, D., Ball, C. E., & Trauman, R. (Eds.). (2016). The new work of composing. Computers and Composition Digital Press.
18. Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
19. Klein, L. F., & Gold, M. K. (2019). Debates in the digital humanities 2019. University of Minnesota Press.
20. Liang, S., Lee, H., & Kim, Y. (2023). Reliability and bias of AI-generated text detection tools in academic contexts. Assessment & Evaluation in Higher Education, 48(9), 1361–1375. https://doi.org/10.1080/02602938.2023.2245850
21. Liu, A. (2022). Friending the past: The sense of history in the digital age. University of Chicago Press.
22. Mathews, N. S., Heidari, S., & Sun, M. (2024). Test-driven development for code generation: Implications for intelligent systems. arXiv preprint arXiv:2402.13521.
23. McKee, H. A., & Porter, J. E. (2021). The ethics of internet research: A rhetorical, case-based process. Peter Lang.
24. McNair, T. B., Albertine, S., Cooper, M. A., McDonald, N., & Major, T. (2020). Becoming a student-ready college: A new culture of leadership for student success. Jossey-Bass.
25. Miller, C. R. (1984). Genre as social action. Quarterly Journal of Speech, 70(2), 151–167. https://doi.org/10.1080/00335638409383686
26. Mitchell, M. (2023). Artificial intelligence: A guide for thinking humans (Rev. ed.). Pelican.
27. National Academies of Sciences, Engineering, and Medicine. (2023). Foundations of workforce readiness in the age of artificial intelligence. National Academies Press.
28. Nosek, B. A., et al. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425. https://doi.org/10.1126/science.aab2374
29. OpenAI. (2023). New AI classifier for indicating AI-written text (retired for low accuracy).
30. Raji, I. D., Bender, E. M., Paullada, A., Denton, E., & Hanna, A. (2022). AI and the everything in the whole wide world benchmark. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 210–223. https://doi.org/10.1145/3531146.3533089
31. Raymond, E. S. (2020). The cathedral and the bazaar: Musings on Linux and open source by an accidental revolutionary. O’Reilly Media.
32. Rashkin, H., Clark, E., Huot, F., & Lapata, M. (2025). Help me write a story: Evaluating large language models’ ability to generate writing feedback. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 1254–1271. https://doi.org/10.18653/v1/2025.acl-long.1254
33. Reynolds, N., & Rice, J. (2023). The rhetoric of algorithms: Teaching digital writing with data ethics. Routledge.
34. Russell, D. R. (1997). Rethinking genre in school and society: An activity theory analysis. Written Communication, 14(4), 504–554. https://doi.org/10.1177/0741088397014004004
35. Selber, S. A. (2004). Multiliteracies for a digital age. Southern Illinois University Press.
36. Sun, N., & Stokke, A. (2023). Integrating AI feedback tools in university writing instruction. Studies in Higher Education, 48(10), 1724–1738. https://doi.org/10.1080/03075079.2023.2199458
37. Theunissen, T., van Heesch, U., & Avgeriou, P. (2022). A mapping study on documentation in continuous software development. Journal of Systems and Software, 191, 111351. https://doi.org/10.1016/j.infsof.2021.106733
38. Zimmerman, B. J., & Kitsantas, A. (2014). Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39(2), 145–155. https://doi.org/10.1016/j.cedpsych.2014.03.004
