From Conventional AI to Modern AI in education- Re-examining AI and Analytics Techniques for Teaching and Learning
Bibliographic record
Abstract
With the rapid development and significant successfulness of various deep learning techniques in artificial intelligence (AI) in recent years, the connotation of AI has been transformed from traditional rule-based or statistical learning models to deep learning models. Such a transformation of AI has led to a significant evolution in both academic and industrial fields. To understand the potential impact of AI evolution for future teaching and learning, it is necessary to re-examine the opportunities, research issues, and roles of AI in education as modern AI enables the possibility of playing vital roles in education, which are not only limited to intelligent tutors/tutees but also intelligent learning partners or policy making advisors. Motivated by the recent transformation and trends in AI in education, this special issue, including 13 research articles, aims to launch an in-depth discussion on re-examining AI and analytics techniques in teaching and learning applications.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".