The Impact of AI on Educational Assessment: A Framework for Constructive Alignment

This paper develops a theoretical framework based on Constructive Alignment theory and Bloom's taxonomy to guide how educational assessment should be adapted in response to students' use of AI tools like LLMs, proposing that different Bloom levels require different assessment approaches when AI is available.

The influence of Artificial Intelligence (AI), and specifically Large Language Models (LLM), on education is continuously increasing. These models are frequently used by students, giving rise to the question whether current forms of assessment are still a valid way to evaluate student performance and comprehension. The theoretical framework developed in this paper is grounded in Constructive Alignment (CA) theory and Bloom's taxonomy for defining learning objectives. We argue that AI influences

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