Effect of Chat GPT on the digitized learning process of university students
Bibliographic record
Abstract
This study's main objective was to determine how the use of ChatGPT has impacted the digitalized education system among Peruvian university students. This study used descriptive statistics and linear regression analysis using the data collected randomly from 216 students’ responses on the Twitter website on the various experiences they have of ChatGPT. According to this research, 71.30% of participants who participated in the discussion agreed that they use ChatGPT as it is fast and provides the most accurate answers. Fifty participants representing 23.15% of the total indicated in the discussion that they use ChatGPT since it is free and easy to use. Additionally, the linear regression analysis to determine its cost, recommendation, rate of task completion and preference as any impact on the usage of ChatGPT and how this affects the digitalized learning process. And from the result, there was a positive correlation between the independent variable of student use of ChatGPT and the dependent variables of the rate of assignment completion, cost, and preference for using ChatGPT because of its services. Given that most students may access ChatGPT for no cost, and its estimated cost variable was 0.379, it is widely used by them. These results prove that ChatGPT significantly impacts the digitalized learning process as many students prefer to use ChatGPT to handle tasks. Therefore, it is clear that institutions should come up with ways of dealing with students' growing use of AI bots.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".