Speaker
Description
This presentation explores a framework for balancing generative AI with the traditional writing process in university English courses. By alternating between analog, pen-and-paper drafting and digital, AI-assisted refining, instructors can mitigate academic integrity risks while teaching responsible technology use. Participants will examine specific task sequences designed to ensure students engage in the essential trial and error necessary for language acquisition while effectively leveraging large language models as collaborative tools.
Abstract
Generative artificial intelligence (AI) offers significant affordances for language learners, particularly in brainstorming and linguistic error correction (Mollick & Mollick, 2023). However, the widespread availability of large language models (LLMs) threatens the integrity of the writing process. When students rely solely on AI for output, they risk bypassing the productive struggle and trial and error essential for second language acquisition (Hyland, 2019). This creates a tension for instructors who recognize the professional utility of AI but wish to preserve the pedagogical value of independent drafting.
This practice-oriented presentation introduces a modular framework used in a first-year university writing context in Japan to resolve this tension. The session demonstrates a curriculum design that purposefully alternates between analog and digital environments. Specifically, the presenter will detail how in-class, pen-and-paper drafting sessions are used to ensure authentic student production. This is followed by structured AI-integrated tasks where students learn to treat LLMs as sophisticated peer-editors and research assistants rather than ghostwriters.
Participants will gain specific strategies for sequencing writing tasks to protect the integrity of the drafting process. They will also receive practical examples of "AI-integrated" prompts that encourage students to critically evaluate machine output. Finally, attendees will learn how to implement "process logs" to track student-AI interaction, ensuring that technology serves to optimize, rather than replace, the student's original voice.
Short summary
This presentation explores a framework for balancing generative AI with the traditional writing process in university English courses. By alternating between analog, pen-and-paper drafting and digital, AI-assisted refining, instructors can mitigate academic integrity risks while teaching responsible technology use. Participants will examine specific task sequences designed to ensure students engage in the essential trial and error necessary for language acquisition while effectively leveraging large language models as collaborative tools.
References
Hyland, K. (2019). Second language writing. Cambridge University Press.
Mollick, E. R., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. arXiv. https://doi.org/10.48550/arXiv.2306.10052
Keywords
Artificial Intelligence
Process Writing
Task Design
L2 Writing
| Scheduling preference | Anytime on Saturday |
|---|---|
| Title | Integrating AI into the writing process: A balance of analog and digital |