What do I Know about AI Beyond Everyday Knowledge?

Advancements in artificial intelligence (AI) make AI literacy essential. However, non-specialist university students often hold misunderstandings of basic AI concepts. This study used a mixed-methods approach to identify which AI concepts are most challenging for novice learners in a college population. Using the Artificial Intelligence Literacy Concept Inventory (AI-CI) and a confirmatory multidimensional Item Response Theory (MIRT) analysis, we found that items in the AI-CI “Machine Learning (ML)” dimension (i.e., a dimension focused on general ML ideas such as learning from data and distinguishing supervised vs. unsupervised learning) were more difficult for participants than items in the other AI-CI dimensions (i.e., What is AI, Decision Trees, Supervised Learning, Generative Adversarial Networks, and Neural Networks). Cognitive interviews further suggested that everyday knowledge supported interpretation of several AI concepts, but many ML items required more technical mental models (e.g., how training data relates to prediction and generalization). These findings highlight prevalent ML-related misconceptions among the students in our study and suggest the need for targeted instruction that explicitly addresses learning-from-data, labeling, and generalization in higher education AI literacy contexts.