Evaluating GPT-enabled learning
Challenges and opportunities for academic performance in a global context
Bibliographic Data
| ID | 6455731 |
|---|---|
| Authors | Nguyen Thi Phuong Thao (0000-0001-5742-8727, Ton Duc Thang University, corresponding author), Do Huu Tam (Van Lang University), Minh Ly Duc (0000-0003-0200-7153, Van Lang University), Nguyen Quang Sang (0000-0003-0239-5264) |
| Year | 2025 |
| Volume | 10 |
| Pages | 101523-101523 |
| Publication date | 2025-11-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Futures (JOURNAL) |
| Journal identifiers | ISSN: 2666-1888 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.sftr.2025.101523 |
| OpenAlex | W7106298988 |
| Language | EN |
| References cited | 67 |
This study investigates how GPT-enabled learning (GPT-HE) influences inclusive education and knowledge management outcomes in higher education. Drawing on the Inclusive Education Challenge (IEC) and Knowledge Management (KM) frameworks, the study integrates the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT) to capture both cognitive and motivational dimensions of GPT adoption. Using a hybrid Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) approach, data from 519 respondents across Vietnam (64.7 %), the United States (21.8 %), and India (13.5 %) were analyzed. The PLS-SEM results show that MindCare (β = 0.317, p < 0.001), Knowledge Development (β = 0.284, p < 0.001), and Knowledge Sustainability (β = 0.229, p < 0.01) significantly enhance GPT adoption, explaining 68.3 % of the variance in the model (R2 = 0.683). The ANN analysis confirms nonlinear reinforcement of these effects, highlighting MindCare as the most influential predictor (normalized importance = 100 %). The findings demonstrate that GPT supports adaptive learning and innovation while posing minor risks of cognitive dependency. Theoretically, this study extends TAM and SDT by integrating nonlinear behavioral dynamics through the hybrid PLS-SEM/ANN model. Practically, it provides data-driven insights for policymakers and educators to design ethical, sustainable, and inclusive AI-assisted learning strategies
Artificial neural network · Cognition · Context (archaeology · Reinforcement learning · Structural equation modeling · Sustainability · Variance (accounting · AI in Service Interactions · Cognitive Abilities and Testing · Technology Adoption and User Behaviour
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| Highly cited | No |
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