Incorporating Bloom’s taxonomy into promoting cognitive thinking mechanism in artificial intelligence-supported learning environments

This journal article explores the incorporation of Bloom's taxonomy into AI-supported learning environments, specifically focusing on chatbot interactions in a study conducted with 25 Year 5 students at an IB PYP school. The students completed tasks of varying complexity and uncertainty using chatbots, and the analysis revealed that Creating and Evaluating were the dominant aspects in their questioning and answering process, while Applying had a significantly low influence. As tasks became more complex and uncertain, students relied more on lower-level thinking, such as remembering and understanding. These findings shed light on the cognitive dynamics of student-AI interactions and provide insights for optimizing the cognitive thinking mechanism in AI-supported learning environments, considering task complexity and uncertainty. This study addresses this gap by applying Bloom's Taxonomy as a thinking framework within chatbot interactions. By leveraging the capabilities of ChatGPT and employing Bloom's Taxonomy, the research seeks to uncover the cognitive relationship between conversational AI and thinking skills in various learning tasks with complexity and uncertainty levels. The insights gained from this study can inform educational practices and contribute to the improvement of AI-supported learning environments.