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

Emma Franchino, Francesco Gariboldi, Alessandro Grecucci, Gianluca Lattanzi, Massimo Stella

TL;DR

The 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.

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

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

TL;DR

The 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.

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
Paper Structure (42 sections, 3 equations, 6 figures, 6 tables)

This paper contains 42 sections, 3 equations, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Complex network relevance of BFMNs and comparisons with Large Language Models
  3. Aims, hypotheses, and research questions
  4. Methods
  5. Participants
  6. Materials
  7. Procedure
  8. Data Availability
  9. Analysis
  10. Behavioural forma mentis networks
  11. Semantic Frames
  12. Network Visualisations
  13. Word Valence and Valence Aura
  14. Emotional Flower
  15. Network Features
  16. ...and 27 more sections

Figures (6)

  • Figure 1: Semantic frames and emotional flowers of the node Exam.
  • Figure 2: Semantic frames of the node Grade.
  • Figure 3: Distribution of Jaccard similarity values between semantic frames of Exam VS Anxiety across samples. Values equal to zero were plotted at $J = 0.001$ for visualisation purposes, so that zero-overlap cases appear as a visible bar.
  • Figure 4: Semantic frames of the node Anxiety.
  • Figure 5: Semantic frames of the node Wellbeing.
  • ...and 1 more figures