A Scenario for the Future of AI and Technology in Public Education
Bibliographic record
Abstract
Using narrative writing techniques, four authors use a “what if” approach to explore a future possible scenario, set in the year 2037, to reflect on looming changes in an increasingly technological human environment. The purpose of this imaginative exercise is not to pinpoint or predict future events, but to highlight large-scale forces that could push the future of learning in key directions. In this story, we follow a young student through a technology-me- diated experience of daily life. As they navigate their learning environment, we observe some of the challenges and opportunities that this student may face including personalized learning technologies, data tracking, and automated decision systems—all tools which come to firmly define this student’s envi- ronment and potential prospects. The authors follow the scenario by breaking down the major influences and forces behind this possible future reality. This analysis provides a view of the ethical dilemmas raised in such a future world and concludes with a series of proposed solutions: proactive governmental in- volvement, a reimagining of industry best practices, and an emphasis on con- text-aware, community-led, future-building initiatives.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".