Research Scale of College Students' Attitude towards Learning under the Influence of Artificial Intelligence
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
Intelligence and information are important elements in the current development of education, where research on the application of artificial intelligence has been a hot topic in recent years. The assessment using a scale is an important method to explore the learning situation of learners. The article combines three dimensions of artificial intelligence, college students' learning status, and ability development to design the scale, and obtains samples through actual surveys to test the scale. The results show that the scale has good reliability and validity, good internal consistency among the items, a good fit to the scale structure, and meets the index requirements of the scale design. The scale is suitable for investigating the influence of artificial intelligence on college students through information-based university teaching, can provide a basis for the application and development of artificial intelligence in colleges and universities, and can provide scientific help for college students to better use artificial intelligence.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| 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 it