From Punch Cards to Prompt Engines: The Shared History of Artificial Intelligence and the Learning Sciences
Bibliographic record
Abstract
Generative AI, in form of Large Language Models such as ChatGPT, has created a significant disruption in educational environments; however, the development of artificial intelligence and education as a field has a rich and storied history. This paper articulates the development of artificial intelligence in education with respect to several periods of development, as well as the two major modes of thought within, cognitivism and constructionism. The paper argues that these two modes of thought defined the landscape of artificial intelligence and that not only has education been a significant point of research in this field, but that learning was inextricably linked to the study of artificial intelligence: the early artificial intelligence researchers, such as Marvin Minsky, Seymour Papert, Herbert Simon, and Allen Newell, developed theories of thought and cognition and built these theories into applications of artificial intelligence and computing; these theories, with particular reference to the work of Minsky and Papert, would directly lead to the development of the learning sciences as a field of research. The paper also provides descriptions of several periods of bust-and-boom within the study of artificial intelligence, a brief review of the status of artificial intelligence in education as a field today, and an analysis of the use of artificial intelligence as a representation of human thought patterns in contemporary research.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".