Math anxiety and associative knowledge structure are entwined in psychology undergraduate students but not in Large Language Models

Math anxiety poses significant challenges for university psychology students, impacting career choices and well-being. This study introduces a framework based on behavioral forma mentis networks (i.e., cognitive models mapping how individuals structure their associative knowledge and emotional perceptions of concepts) to explore individual and group-differences in the perception and association of concepts related to math and anxiety. We conducted 4 experiments involving psychology undergraduates from 2 samples (n1 = 72, n2 = 79) compared against GPT-simulated students (n3 = 300). Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety and its facets (observational, social and evaluational) from the Math Anxiety Scale. Experiment 4 focuses on group-level perceptions extracted from students’ and GPT-3.5’s networks. Results indicate that, in students, positive valence ratings and higher network degree for “anxiety” together with negative ratings for “math” can predict higher total and evaluational math anxiety. In contrast, human models do not work on GPT-based data because of differences in simulated networks and psychometric scores compared to humans. These results were reconciled also with differences found in the ways that high/low subgroups of simulated and real students framed semantically and emotionally STEM concepts. High math-anxiety students collectively framed “anxiety” in an emotionally polarizing way, absent in the negative perception of low math-anxiety students. “Science” was rated positively but contrasted against the negative perception of “math”. These findings underscore the importance of understanding concept perception and associations in managing students’ math anxiety.