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    Designing an Integrated Multi-Dimensional Assessment Framework for AI-supported Academic Writing
    The widespread adoption of generative AI is transforming academic writing in higher education, rendering traditional, product-focused assessment models obsolete. These methods fail to capture the iterative and tool-mediated nature of modern writing processes, creating an urgent need for new evaluation approaches. This paper addresses this gap by proposing an integrated, multi-dimensional assessment framework. Grounded in genre pedagogy, self-regulated learning, and writing analytics, our model conceptualizes assessment as a holistic process. It evaluates foundational skills for AI use (Computational Thinking and Genre-Knowledge), the quality of text revision, final writing performance, and long-term development. By aligning these dimensions with formative, summative, and diagnostic purposes, the framework fosters transparency, metacognitive engagement, and responsible AI use. We are preparing a design-based research project to pilot and iteratively refine key elements of the framework within a mastery learning programme for 1,800 first-year students. The goal is to explore how the model can be implemented in practice and refined through iterative cycles of design, enactment, and evaluation.
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