Human-in-the-loop in artificial intelligence in education: A review and entity-relationship (ER) analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human-in-the-loop research predominantly examines the interaction types and effects. A more structural and pragmatic exploration of humans and Artificial Intelligence or AI is lacking in the artificial intelligence in educational literature. In this systematic review we follow the Entity-Relationship (ER) framework to identify trends in the entities, relationships, and attributes of human-in-the-loop AI in education. An overview of N = 28 reviewed studies followed by their ER characteristics are summarized and analyzed. The dominant number of two or three-entity studies, one-sided relationships, little attributes, and many to many cardinalities may signal a lack of deliberation on beings that come to interact and influence human-in-the-loop and AI in education. The contribution of this work is identifying the implications of human-in-the-loop and AI from a more formal ER perspective and acknowledging the many possibilities for placement of humans in the loop with the AI, system, and environment of interest.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it