Bibliographic record
Abstract
The advent of technology has dramatically reshaped the ways in which we assimilate knowledge, teach, and access information. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a vibrant, engaging, and inclusive educational landscape. A notable advancement in artificial intelligence technology is ChatGPT (Generative Pre-trained Transformer), which provides personalized and effective learning experiences by delivering customized feedback and explanations to students. Despite the considerable research on the adoption or acceptance of e-learning, there is a paucity of research on the acceptance and utilization of ChatGPT, highlighting the need for further investigation. This study aims to bridge this gap by proposing an integrated model that incorporates three key constructs: perceived learning value, perceived satisfaction, and personal innovativeness. A questionnaire survey was administered to 289 university students in the United Arab Emirates (UAE), and the data collected were analyzed using the partial least squares-structural equation modeling (PLS-SEM) approach. The results revealed that "perceived learning value, perceived satisfaction, and personal innovativeness" are the most influential and critical determinants of students' intentions to use learning platforms through ChatGPT. This research contributes to the existing body of literature on AI and environmental sustainability, providing invaluable insights for practitioners, policymakers, and AI product developers. These insights can guide the development and implementation of AI technologies to better align with users' needs and preferences, while also considering the broader environmental context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".