Quantifying Assessment Difficulty in GenAI-Enhanced Education: Integrating Reinterpreted RBT and SOLO Taxonomy

Generative AI (GenAI) has posed challenges for traditional assessment frameworks, necessitating innovative approaches to effectively quantify assessment difficulty and distinguish authentic student learning from AI-assisted outcomes. This paper proposes a novel integration of the reinterpreted Revised Bloom's Taxonomy (RBT) and the Structure of Observed Learning Outcomes (SOLO) taxonomy. Moving beyond RBT's traditional hierarchical structure, we develop a radar chart visualization that recognizes how assessment tasks can simultaneously engage multiple cognitive processes with varying complexity levels. The framework offers a nuanced approach to defining and evaluating assessment difficulty, measuring both the breadth of cognitive skills and the depth of thinking structure, along with knowledge domains. Implementation examples demonstrate how this mapping can be systematically applied across various contexts and multiple scales-from individual tasks to comprehensive learning programs, with the potential for automating competency assessment processes. By establishing a quantitative framework for assessing cognitive complexity, this paper offers educators a systematic and standardized method to understand and measure student learning in GenAI-enhanced educational environments.