Artificial Intelligence (AI), Sustainability and Engineering Education: Implementation trends in STEM to realize Society 5.0.
Bibliographic record
Abstract
Sustainability-centered engineering is an attribute of Society 5.0, which, in turn, impacts Industry 5.0 (I.D. 5.0) requirements and Education 5.0 (E.D. 5.0) skills within STEM. With the ever-changing global industry landscape, it is imperative to map critically and comprehensively assess (i) the impact of sustainability on E.D. 5.0/I.D. 5.0, (ii) the impact(s) of AI on sustainability measures between the Global North and the Global South economic blocs, and (iii) how these initiatives measures impact Higher Education Institutes (HEIs) in STEM teaching/research. To obtain these perspectives, a bibliometric analysis was performed (2014-2024) on Scopus, based on Research Questions (RQs) from the literature, to capture (i) the existence of an AI gap between the Global North and South, (ii) a lack of current initiatives in sustainability in HEIs in STEM, (iii) an imminent need to promote sustainability-oriented curricula design across HEIs, and (iv) identify novel pedagogical strategies to foster such targeted learning.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".