"Smarter but Not Wiser": A Transdisciplinary CFA-GRM Simulation Pipeline for Mapping Human–GenAI Interaction
Background The integration of Generative AI (GenAI) in higher education creates a paradox between technical efficiency and cognitive depth. Current institutional responses rely more on fragmented oversight than on ecology- and process-oriented assessment. There is a lack of diagnostic tools to map psychometric thresholds when strategic cognitive shifts transition into functional dependence. Methods We present a simulation-based methodological framework using the Tripartite Confirmatory Factor Analysis–Graded Response Model (CFA-GRM). Grounded in the cognitive-affective-conative tripartite model, this study uses a Monte Carlo simulation (N = 1,000) to demonstrate how latent interaction pathways can be structurally validated and how item parameters can be calibrated against a pre-specified diagnostic threshold ( θ ≈ 0.5), with a replication study to establish parameter recovery. Results Across 200 replications at each of N = 1,000 and N = 300, all 400 converged with admissible solutions and recovered the generating parameters with maximum absolute relative bias of 0.040; interval coverage met conventional criteria for 11 of 15 parameter groups at N = 1,000 and 8 of 15 at N = 300. In the single illustrative dataset, the fit is near-perfect (robust CFI = 0.995, RMSEA = 0.010) because the generating and estimated models coincide, a property of the design rather than evidence for the model. Test information peaks at θ ≈ 0.07–0.22, not at the proposed threshold of θ ≈ 0.5. Furthermore, we introduce a Transdisciplinary Calibration Matrix that translates these psychometric signals into context-specific pedagogical interventions across four epistemic layers, in line with SDG 4. Conclusions This study does not claim empirical generalization. It provides a methodological blueprint, a recovered parameter set, and a shared diagnostic language for future empirical work on the ethical and adaptive integration of GenAI in higher education, aligned with SDG 4.
Authors
- Ifan Rivaldo
- Syarifah Niken Purnani
- Kana Hidayati (ORCID: https://orcid.org/0000-0002-9226-8500)
- Nur Sehang Thamrin (ORCID: https://orcid.org/0000-0001-5508-0886)
- Tri Susanti (ORCID: https://orcid.org/0000-0003-4684-093X)
- Putri Theresia Oktaviani Manorek
- Barquna Tri Raraswati
- Juverio Pangestu (ORCID: https://orcid.org/0009-0005-1510-6371)
- Achmad Fatahillah
- Chalimatus Sa'diyah
- Rina Nopita Manullang
- Jacinto Soares da Silva
- Amirudin Amirudin
Institutions
- Yogyakarta State University (ID)
- Tadulako University (ID)
- Universitas Gadjah Mada (ID)
- Sepuluh Nopember Institute of Technology (ID)
- State University of Padang (ID)
- Universitas Sumatera Utara (ID)
- University of Bangka Belitung (ID)
- Badan Pusat Statistik (ID)
Publication Details
- Journal
- F1000Research
- Published
- 2026-10-08
- DOI
- https://doi.org/10.12688/f1000research.190208.1
- Primary Topic
- Psychometric Methodologies and Testing
- Type
- article
- Field-Weighted Citation Impact
- 0.00