Leveraging generative AI for course learning outcome categorization using Bloom's taxonomy

Learning outcomes are clear and concise statements that describe what students should be able to do or know at the end of a particular course. These statements are crucial in instructional planning, curriculum development, and assessment of student progress and learning. Although there is no universal guidance on how to develop learning outcomes, Bloom's taxonomy is one widely used framework that helps instructors develop outcomes that reflect different levels of thinking, from basic remembering to creative problem-solving. This study investigates the potential of generative AI, specifically GPT-4, in classifying course learning outcomes according to their respective cognitive levels within the revised Bloom's taxonomy. To assess the effectiveness of GenAI, we conducted a comparative study using a dataset of 1000 annotated learning outcomes. We tested multiple prompt engineering strategies, including zero-shot, few-shot, chain-of-thought, rhetorical situation, and multiple binary questions, leveraging GPT-4. Classification performance was evaluated using accuracy, Cohen's κ, and F1-score. The results indicate that the prompt incorporating rhetorical context and domain-specific knowledge achieved the highest classification performance, while the multiple binary question approach underperformed even compared to the zero-shot method. Furthermore, we compared the best-performing prompting strategy with a state-of-the-art classification model, BERT. Although the fine-tuned BERT model showed superior performance, prompt-based classification exhibited moderate to substantial agreement with expert annotations. Overall, this article demonstrates the potential of leveraging large language models to advance both theoretical understanding and practical application within the field of education and natural language processing.