Research on the Application of Artificial Intelligence (AI) in (K-14 to K-18) Art Education
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
This paper investigates the educational integration of Artificial Intelligence (AI) in high school art education and examines AI's roles in teachers' and students' classrooms. A snowball sampling method was selected, covering 25 participants from developed areas in China, including six teachers and 19 students. Participants are categorized by familiarity with AI tools such as ChatGPT and DALL-E, which are conducted as an assessment and creative exercise. The results showed that, although 55% of teachers used AI to offer formative feedback, 24% feared that students might become overly dependent on AI scribes and lose some of their spontaneous creative instincts. Meanwhile, 52% of students said AI helped them more effectively generate and refine creative documents. The study concluded that, when AI comes to art education, it can be both a boon and a bane: it has the potential to enhance creativity but may threaten the integrity of the learning process
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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