Generative artificial intelligence (GenAI) can enhance students’ efficiency and the apparent quality of academic work, yet its fluent outputs may encourage learners to outsource cognitive activities central to learning. This creates the risk of “performance without learning”, whereby successful task completion does not necessarily reflect meaningful understanding or learner regulation. To address this concern, this article develops the Intent, Deconstruction, Expression, and Adaptation (IDEA) framework, a theory-informed metacognitive scaffold for GenAI-supported academic work. IDEA guides learners to articulate goals and constraints, decompose tasks into essential processes, communicate requirements through structured prompts, and critically evaluate and iteratively refine AI-generated outputs. By embedding prompting within a cycle of planning, monitoring, and regulation, IDEA provides an actionable approach for preserving learner agency in GenAI-supported work. An exploratory quasi-experimental pilot study with 42 undergraduates examined the feasibility and preliminary educational value of IDEA-based instruction relative to structured prompt-engineering instruction. Across immediate GenAI-assisted academic tasks, IDEA-trained students produced higher-quality prompts and final AI-generated outputs, and their interaction records showed observable enactment of the framework’s core activities. On unaided tasks completed five days later, the IDEA group demonstrated advantages on selected tasks. Together, the conceptual framework and pilot findings position IDEA as a practical instructional approach for transforming GenAI use from passive content outsourcing into deliberate, evaluative, and learner-regulated interaction. Future research can examine its operation across disciplinary contexts and its longer-term implications for learning.
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