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Title:
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DESIGNING AN INTEGRATED MULTI-DIMENSIONAL ASSESSMENT FRAMEWORK FOR AI-SUPPORTED ACADEMIC WRITING |
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Author(s):
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Sabine Seufert and Niklas Eulitz |
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ISBN:
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978-989-8704-72-6 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler and Pedro Isaías |
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Year:
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2025 |
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Edition:
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Single |
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Keywords:
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Generative AI, Academic Writing, Writing Analytics, Evidence-Centered Design (ECD), Computational Thinking (CT) |
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Type:
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Full Paper |
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First Page:
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119 |
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Last Page:
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127 |
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Language:
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English |
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Cover:
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Full Contents:
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Paper Abstract:
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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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