GenAI-empowered teachers as active actors of change in developing (inter)national human capital in Singapore
Bibliographic record
Abstract
The integration of Generative Artificial Intelligence (GenAI) within education technology (EDTech) aims to transform Singapore into a global hub for GenAI by 2030. This means that schools, including postsecondary and higher institutions, must support this shift, and teachers must become key facilitators in transferring these new experiences to their students. Using Bourdieu’s Capital Theory, this chapter article discusses how the shift towards GenAI-infused teaching will revolutionise the education landscape, particularly in the teaching and learning of English, and improve enhance Singapore’s human capital, including international students living in Singapore, at the macro level. However, given the varying levels of English-language competency among students from different socioeconomic backgrounds and countries, this shift could lead to more significant social inequalities and stratification, as GenAI could act as both a leveller and a limiter. This paper article highlights that while GenAI empowers the education landscape and becomes a ‘non-human’ actor in this transformative journey, AI-empowered teachers become critical for effective language acquisition, as traditional teaching ensures not only personalised learning and a contextualised understanding of English but also acts as a social leveller.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".