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Beyond Co-Regulation: Interplay as a Methodological Framework for Examining Self-Regulation in Generative AI-Assisted Writing.

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Title: Beyond Co-Regulation: Interplay as a Methodological Framework for Examining Self-Regulation in Generative AI-Assisted Writing.
Authors: Roderick, Ryan1 (AUTHOR) roderickr@husson.edu, Tanner, Susan2 (AUTHOR)
Source: Written Communication. Jul2026, Vol. 43 Issue 3, p798-817. 20p.
Subject Terms: *Generative artificial intelligence, *Writing processes, Self regulation, Human-artificial intelligence interaction
Abstract: As generative artificial intelligence (GenAI) tools become embedded in writing practices, researchers must refine methodologies for studying self-regulation in AI-assisted composition. While sociocognitive and co-regulation frameworks have effectively captured self-regulatory processes in human collaboration, they are insufficient for understanding how writers manage the dynamic and probabilistic nature of AI-generated text. This article introduces interplay as a methodological framework to analyze the recursive process of initiating, responding, adapting, and revising in human–AI writing interactions. Unlike co-regulation, where collaborators share communicative intent, interplay highlights the writer's active role in interpreting and steering AI-generated content. Drawing on self-regulation theory, we propose an analytical framework that integrates traditional self-regulation categories (goal-setting, monitoring, and reflection) with interplay-specific coding (initiation, evaluation, acceptance, and adaptation). Through case analyses of human–AI writing exchanges, we demonstrate how interplay provides a systematic approach to studying agency, decision making, and regulatory strategies in AI-assisted writing. We argue that recognizing interplay as a distinct dimension of self-regulation advances both empirical research and pedagogical approaches to AI-mediated composition. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract:As generative artificial intelligence (GenAI) tools become embedded in writing practices, researchers must refine methodologies for studying self-regulation in AI-assisted composition. While sociocognitive and co-regulation frameworks have effectively captured self-regulatory processes in human collaboration, they are insufficient for understanding how writers manage the dynamic and probabilistic nature of AI-generated text. This article introduces interplay as a methodological framework to analyze the recursive process of initiating, responding, adapting, and revising in human–AI writing interactions. Unlike co-regulation, where collaborators share communicative intent, interplay highlights the writer's active role in interpreting and steering AI-generated content. Drawing on self-regulation theory, we propose an analytical framework that integrates traditional self-regulation categories (goal-setting, monitoring, and reflection) with interplay-specific coding (initiation, evaluation, acceptance, and adaptation). Through case analyses of human–AI writing exchanges, we demonstrate how interplay provides a systematic approach to studying agency, decision making, and regulatory strategies in AI-assisted writing. We argue that recognizing interplay as a distinct dimension of self-regulation advances both empirical research and pedagogical approaches to AI-mediated composition. [ABSTRACT FROM AUTHOR]
ISSN:07410883
DOI:10.1177/07410883261440232