Education, work and Australian society in an AI world: A review of research literature
Bibliographic record
Abstract
Research Report—‘Education, work and Australian society in an AI world: A review of research literature’ 1. Research Background The research was in the field of education. The Gonski Institute for Education commissioned us to research and write a report on current research literature concerning artificial intelligence and education. The emergence of Artificial Intelligence has become an issue for education in two ways—in terms of changing provision and practices within education itself, and in terms of education’s crucial role in preparing for the future, especially but not only the future of work. Policy makers are seeking guidelines as to how to respond. The report’s key aim was to review the literature in order to present policy recommendations. 2. Research Contribution The report contributes to knowledge by synthesising and evaluating the current literature and presenting 6 key policy recommendations in order to safeguard the future of Australian education and to foster social innovation. Its main concern is the organisation and administration of education in a world in which AI is becoming an increasingly powerful social force. The recommendations include: forming a cross-sector representative body; providing professional development opportunities for teachers; working towards the ethical and effective procurement of AI systems; introducing adaptive and personalised learning in a way that ensures a focus on educational and equity principles; providing appropriate data protection and legislation; focusing on AI-complimentary skills. 3. Research Significance & Evidence of Excellence The significance of this research is that it enables education policy influencers, policy makers and other stakeholders to frame appropriate responses to the emergence of AI. Its value is attested to by the following indicators: • Accepted and published by the Gonski Institute for education • Invited to publish an article based on the report for the Journal of Professional Learning published by The NSW Teachers Federation’s (NSWTF) Centre for Professional Learning (CPL) [written and published January 2019] • Has received responses international from various teacher’s group (e.g. from Canada)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".