The adoption of ChatGPT marks the beginning of a new era in educational platforms
Bibliographic record
Abstract
Technology has significantly transformed knowledge, education, and access to information by introducing online learning platforms, interactive games, and virtual reality simulations in traditional classrooms, creating a dynamic, engaging, and inclusive learning environment. The ChatGBT project (a pre-developed transformer for training) is a remarkable achievement in artificial intelligence technology. It allows students tailored and efficient learning experiences by providing individual feedback and explanations. ChatGPT e-learning platform has been extensively studied for its adoption and acceptance, but there is a significant gap in research on its acceptability and use, highlighting the need for further exploration. The goal of this work is to bridge this disparity by introducing a comprehensive model that includes three basic elements: performance expectation, expected effort, and social impact. A total of 241 graduate students were surveyed and their data were analyzed using structural equation modeling techniques. The results indicate that “expectation of performance and expected effort” have the greatest impact and importance in determining students’ intentions to use learning platforms via ChatGPT, while social influence does not play an important role. This study enhances the current body of knowledge related to artificial intelligence and environmental sustainability, and provides important insights for professionals, policymakers, and producers of artificial intelligence products. These observations may provide guidance for creating and implementing artificial intelligence technologies to match consumers’ needs and preferences more effectively, while also taking into account broader environmental conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".