Benchmarking Engineering Management Graduate Program at the Onset of the AI Era
Bibliographic record
Abstract
This research delves into the evaluation of graduate programs in Engineering Management as they relate to the rapidly changing landscape of Artificial Intelligence (AI). As AI continues to revolutionize industries and workforce dynamics, this study aims to determine the effectiveness of engineering management programs in accommodating these advancements. The underlying concept of this study is built on the premise that engineering management programs must evolve to meet the challenges and opportunities presented by AI. The research employs a mixed-methods approach, combining qualitative interviews with program administrators, alumni, faculty, and industry professionals with quantitative analyses of curriculum structures, technological integration, and student outcomes. Through an extensive literature review, the research contextualizes the influence of AI on engineering management practices and identifies key competencies and knowledge areas crucial for graduates. Data collection involves surveys and interviews to gather insights into program strengths, weaknesses, and areas requiring enhancement. Findings present comparative analysis of factors such as the incorporation of AI-related coursework, industry partnerships fostering AI applications, and the adaptability of programs to emerging trends.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".