Evaluation of the Influence of Artificial Intelligence on College Students' Learning Based on Group Decision-making Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid development of artificial intelligence technology is transforming people's lifestyles and work patterns across various fields. In the realm of education, it also exerts an influence on the learning experiences of university students. To comprehend the multifaceted impact of artificial intelligence on university students' learning, this paper collected feedback results from a survey on artificial intelligence. Through statistical analysis and differentiation of survey data, focusing on prioritization, scientificity, operability, and rationality, we identified several evaluation indicators that best reflect the impact of artificial intelligence on university students in this survey. Subsequently, by establishing models based on the data and considering the weights and impact levels of different indicators, we utilized group decision methods to quantitatively assess the most crucial aspects of the influence of artificial intelligence on university students' learning. The analysis results provide a comprehensive evaluation of the potential impact of artificial intelligence learning tools on university students' learning.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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 it