Research on the Teaching Reform Path for the Intelligent Construction Major Based on the OBE Concept and Empowered by AI
Bibliographic record
Abstract
With the rapid advancement of artificial intelligence (AI) technology and the swift rise of the intelligent construction industry, the architecture and construction sector is undergoing unprecedented transformation. In this context, cultivating high-quality intelligent construction professionals who can meet the demands of the new era has become a critical issue in higher education reform. Guided by the Outcome-Based Education (OBE) concept and empowered by AI technology, this paper proposes a closed-loop reform model that treats OBE as the framework and AI as the tool. It elaborates on the integrative logic of OBE and AI empowerment, and explores innovative pathways for teaching reform in intelligent construction education. These include restructuring the curriculum system, innovating teaching methods, enhancing practical training, and reforming evaluation mechanisms, thereby providing theoretical support and practical guidance for the pedagogical reform of intelligent construction programs.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".