Preface to the IJAIED 25th Anniversary Issue, Part 2
Bibliographic record
Abstract
AIED: The Next 25 YearsWe are very pleased to present part 2 of the 25th anniversary special issue of the International Journal of Artificial Intelligence in Education (IJAIED) to which we have given the title: The Next 25 Years: How Advanced Interactive Learning Technologies will Change the World.Part 1 of this special issue stands, in the words of the guest editors, as a Bcelebration of the scholarly kind^of the journal's past 25 years and the 400+ articles published.It is our honor in part 2 to continue the celebration and share the vision of some of AIED's current and emerging leaders who accepted our invitation to look forward to the next 25 years of learning technology research.This issue is framed by Part 1 of the special issue, which consists of 35 articles written by authors of the most highly-cited and influential articles from the first 25 years of IJAIED.These authors reflect on their past work, identify its key contributions and impacts, and discuss how the work evolved after the original publication.Importantly, authors were also asked to consider remaining open questions and what would be needed today in order to solve the problems they had originally pursued.It is with these Bremaining open questions^that Part 2 of the anniversary issue picks up.We focus on the road ahead for AIED, what new research
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.165 | 0.104 |
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".