Past, Present and Tackling the Future of Artificial Intelligence (AI) in Education: Maintaining Agency and Establishing AI Laws
Bibliographic record
Abstract
The urgency to establish laws for using generative artificial intelligence (GAI) is upon our society. At the end of the year, 2022 OpenAI made available to an international public its ground-breaking software, ChatGPT which is utilized by 1.8 billion users per month. Never before has a technology application been so successful so quickly. In this paper, the author outlines a history of artificial intelligence (AI), discusses ways in which generative artificial intelligence (GAI) technologies are used today, and delineates the future use of GAIs in education for all areas of study. A focus is on analyzing the advantages and disadvantages of GAIs with particular attention to the consideration of human agency versus machine agency. The author examines ways to avoid problems using GAIs currently. Also considered are ways in which human beings can use GAIs in the future while maintaining their own power, autonomy and control. To support this, Marshall McLuhan’s laws for the electronic media are revised as “Laws of Generative Artificial Intelligence” to aid educators from kindergarten to higher education for teaching in the “GAI Era”.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".