Construction and Practical Exploration of an AIGC-Assisted Project-Based Teaching Model
Bibliographic record
Abstract
Although traditional project-based learning (PBL) has proven effective in improving student engagement and practical ability in mechanical design related courses, it still suffers from weak alignment between course projects and real engineering practices, insufficient process evaluation, and limited personalized guidance. To address these issues, this study explores an AIGC-assisted PBL model. In the instructional design, AIGC is integrated throughout pre-class preparation, in-class teaching, after-class assignments, and group projects, supporting students in rapidly acquiring knowledge, generating design schemes, and conducting iterative optimization. Teaching practice shows that this model yields positive results in knowledge acquisition, ability development, and engineering literacy, effectively alleviating the pain points of traditional PBL. However, it is also observed that students' critical thinking and awareness of academic integrity still require further reinforcement. This AIGC-assisted PBL model provides a feasible pathway for the deep integration of "artificial intelligence + education" and offers valuable insights for curriculum reform under the background of emerging engineering education.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".