VALIDATING A METACOGNITIVE ACADEMIC WRITING STRATEGIES SCALE FOR THE AI ERA: A META-SYNTHETIC ANALYSIS

Abstract

Metacognitive academic writing strategies help writers plan, monitor, evaluate, and regulate the production of scholarly texts. Generative artificial intelligence (AI) changes the conditions under which these strategies operate: writers can delegate language-level tasks, consult AI for feedback, compare alternative formulations, and receive rapid explanations, but they must also evaluate inaccurate outputs, preserve authorial agency, and disclose assistance appropriately. This article develops an evidence-informed framework for validating a Metacognitive Academic Writing Strategies Scale for the AI era (MAWSS-AI). It synthesizes the supplied 2022–2025 bibliography on metacognitive writing, academic writing pedagogy, AI-assisted writing, AI literacy, and research integrity. Because the available source is a reference list rather than a complete corpus of full-text studies, the synthesis is conceptual and bibliography-led; it does not claim a completed systematic review, effect-size meta-analysis, or empirical psychometric validation. The proposed framework retains the core regulatory cycle of planning, monitoring, evaluating, and revising, while adding AI-task orchestration, critical verification, human–AI decision-making, and transparency/ethical accountability. A provisional 30-item pool is organized into six dimensions and accompanied by a construct map, synthesis matrix, content-validation rubric, and staged validation protocol. The article recommends expert review, cognitive interviews, pilot testing, exploratory and confirmatory factor analyses, reliability estimates, convergent and discriminant validity, criterion-related evidence, measurement invariance, and response-process checks. The central proposition is that AI-era metacognitive competence is not measured by frequency of AI use, but by the quality of writers’ strategic decisions before, during, and after interacting with AI. The scale should therefore assess intentionality, epistemic vigilance, ownership, and responsible regulation rather than technological enthusiasm alone.