Complex networks map test anxiety and wellbeing levels in students and ChatGPT

TL;DR

This study uses behavioral forma mentis networks to map how students and AI perceive exams and wellbeing, revealing differences in emotional framing and anxiety levels across groups.

Contribution

It introduces a novel network-based method to quantitatively analyze semantic and emotional framings of academic concepts in humans and AI.

Findings

Students associate exams with negative emotions and fear.

GPT-based simulations show less evidence of test anxiety.

Humans associate wellbeing with concrete positive concepts.

Abstract

Academic STEM evaluation can elicit anxiety, yet routine grading rarely captures how students semantically frame exams and wellbeing. We reconstruct these framings using behavioural forma mentis networks (BFMNs), that is, feature-rich networks of concepts linked by memory recalls and enriched with affective ratings and concreteness norms. We build BFMNs from 994 participants spanning STEM experts (N1 = 59), Italian high-schoolers (N2 = 206), physics undergraduates (N3 = 10), psychology undergraduates with math-anxiety levels (N4 = 301), and simulated students (N5 = 497) personified by a large language model (GPT-OSS 20B). Across all human groups, the concepts "exam" and "grade" were (i) perceived negatively, (ii) connected primarily to negatively valenced memory recalls, indicating a clustering of negative emotions around assessment, and (iii) framed through concepts eliciting fear and anticipation in most groups, including physics undergraduates (z-scores in the range [2.04, 2.53]). The semantic neighbourhoods of "anxiety" and "exam" overlapped three times more in human students than in GPT-based simulations, providing structural evidence of test anxiety in student populations. By contrast, experts displayed a neutral and more concrete network neighbourhood for "exam" (z = 1.87), with no clear trace of test anxiety. These negative assessment framings coexisted with positive representations of "wellbeing", which were rich in concrete associations in humans but linked to more abstract concepts in GPT digital twins. Overall, our results show that BFMNs offer a quantitative and interpretable framework to study academic anxiety and to distinguish human affective framing from current AI-based simulations