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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]
Copyright of Written Communication is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: 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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  Data: <i>Copyright of Written Communication is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Self regulation
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              Text: Jul2026
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