Deep Learning-Driven Curriculum Innovation and Structural Optimization of Art and Design Education in Artificial Intelligence Environment
Bibliographic record
Abstract
Driven by artificial intelligence and deep learning technology, this study proposes an intelligent course recommendation system for art and design education.By constructing XMMC, a joint extraction model of knowledge entities and relations based on deep learning, the accurate analysis of course knowledge structure is realized.Key features such as user preference, content semantics and social influence are extracted by combining multi-feature ranking models such as collaborative filtering, topic modeling and course hotness.Finally, based on the deep reinforcement learning algorithm DDPG, a dynamic recommendation strategy is designed to optimize the recommendation effect.The experiments are based on Coursera Course, Caltech-UCSD Birds 200 and Education Recommendation datasets, and the results show that the improved DDPG model achieves 49.11%, 70.05% and 59.23% course coverage on the three datasets, respectively, which is better than the traditional algorithms TimeSVD and CDAE with significant improvement.We constructed the art education course category with the number of topics as 5.In the practical application, the recommended list generated by the system is highly consistent with the course heat analysis, in which the course "Introduction to 3D Modeling and Blender" ranks the first with 6729 average playbacks, which verifies that the recommendation strategy can effectively improve the fitness of the pushed content and the current course progress of the students.It verifies that the recommendation strategy can effectively improve the compatibility between the pushed content and the students' current course progress.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".