Assessing AI-Generated Questions' Alignment with Cognitive Frameworks in Educational Assessment
This paper evaluates whether AI-generated multiple-choice questions (MCQs) can be accurately aligned with Bloom's Taxonomy cognitive levels using various classification models, including DistilBERT, to improve automated question generation in the OneClickQuiz Moodle plugin. The study develops a dataset of 3,691 questions categorized by Bloom's levels and assesses model effectiveness in classifying questions by cognitive complexity.
This study evaluates the integration of Bloom’s Taxonomy into OneClickQuiz, an Artificial Intelligence (AI) driven plugin for automating Multiple-Choice Question (MCQ) generation in Moodle. Bloom’s Taxonomy provides a structured framework for categorizing educational objectives into hierarchical cognitive levels. Our research investigates whether incorporating this taxonomy can improve the alignment of AI-generated questions with specific cognitive objectives. We developed a dataset of 3691 ques