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

This paper develops a theoretical framework based on Constructive Alignment and Bloom's taxonomy to guide how educational assessment should adapt when students use AI tools like LLMs, examining whether current assessment methods remain valid and how AI affects evaluation of learning objectives at different cognitive levels.

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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