Mapping the Shifting Dynamics of Mathematics Learning in the Artificial Intelligence Era: A Psychometric Network Analysis of the Community of Inquiry

Asmaun Asmaun; Fajar  Arwadi; Hamda Hamda; Intan Buhati  Asfyra

doi:10.63922/ijevss.v5i04.6482

Asmaun Asmaun Mathematics Education Study Program, Universitas Negeri Makassar, Makassar, Indonesia
Fajar Arwadi Mathematics Education Study Program, Universitas Negeri Makassar, Makassar, Indonesia
Hamda Mathematics Education Study Program, Universitas Negeri Makassar, Makassar, Indonesia
Intan Buhati Asfyra Mathematics Education Study Program, Universitas Negeri Makassar, Makassar, Indonesia

DOI: https://doi.org/10.63922/ijevss.v5i04.6482

Keywords: Shifting learning dynamics; Generative artificial intelligence; Community of inquiry; Network psychometrics; Metacognitive monitoring; Calculus learning.

Abstract

The integration of generative artificial intelligence (AI) into undergraduate mathematics classrooms has begun to shift the dynamics of the learning ecosystem — reconfiguring how reflective interaction, metacognitive regulation, and cognitive engagement relate to one another within the Community of Inquiry (CoI) framework. Latent-variable structural models have documented that this reconfiguration exists, but they cannot pinpoint which specific learner behaviors carry the shift. This study applies psychometric network analysis to produce a behavior-level map of the reconfigured ecosystem. Using cross-sectional data from 200 undergraduate mathematics education students at Universitas Negeri Makassar who used conversational AI during a differential calculus course, we estimated a regularized partial-correlation network over 19 items from an AI-Integrated CoI survey using the Graphical Lasso with cross-validated penalty selection. Node strength and bridge strength were computed to identify hub and boundary-spanning behaviors, and bootstrap resampling (300 iterations) was used to assess stability. The network retained 87 edges (density = 0.51). The four items with the highest bridge strength were RMP2 (using AI to check one's own solution steps — 0.45), LP3 (monitoring understanding of AI explanations — 0.45), RMP5 (asking follow-up questions to deepen understanding — 0.40), and LP6 (reflecting on whether AI is helping — 0.39). Three of these four top-ranked items describe monitoring and verification behaviors that occur during the AI interaction rather than the post-hoc evaluation of AI outputs most often emphasized in AI-literacy discourse. Bootstrap Spearman correlation between bootstrapped and reference bridge rankings averaged 0.80, indicating moderate stability. The findings reframe the AI-induced shift as a shift toward in-interaction epistemic monitoring rather than a shift toward output-checking vigilance.

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