What Do I Know about AI beyond Everyday Knowledge? Unveiling Misconceptions Using Item Response Theory Analyses and Cognitive Interviews
Year: 2026
Type: article
Study design: Observational Study
Abstract: Advancements in 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 AI 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 the 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.
DOI: 10.1145/3803805
Authors: Xingyao Xiao, Yihong Cheng
Institutions: Stanford University, City University of Macau
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7140914629