Advancing Architecture and Engineering Education for Project Value Delivery
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
Delivering project value primarily depends on understanding project stakeholders' different needs and requirements and translating these needs into a well-constructed facility. This concept is usually insufficiently revealed using different terminologies during the educational journey of architecture, engineering, and construction (AEC) professionals. The goals of this research are to (1) investigate students' and practitioners' familiarity and knowledge about the concept of value, (2) explore underlying gaps in teaching value in AEC education, and (3) propose essential practices to overcome identified educational shortcomings. For this purpose, combined qualitative and quantitative approaches were used to evaluate responses by students and practitioners, including a structured survey, interviews, and statistical analysis. The paper introduced a framework for educational content that supports value delivery using lean principles, design thinking, sustainability, and digital collaborative technologies. The survey and interviews revealed a major deficiency in students' and practitioners' familiarity with the concept of delivering value and the tools needed to enhance it. Thus, a knowledge gap about delivering project value was identified in AEC curricula. Additionally, cross-disciplinary engagement and collaboration efforts were found to be insufficient. Students and practitioners revealed doubts about the relevance of academic projects. Nonetheless, participants confirmed the importance of providing a better understanding of the value concept and related practices. The proposed framework for better incorporating the value concept into AEC curricula has the potential to improve project outcomes and satisfaction in the AEC industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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