Global Trends in Education: Artificial Intelligence, Postplagiarism, and Future-focused Learning for 2025 and Beyond – 2024–2025 Werklund Distinguished Research Lecture
Bibliographic record
Abstract
In this distinguished research lecture, Dr. Sarah Elaine Eaton explores how artificial intelligence (AI) is transforming global education and reshaping our approach to teaching, learning, and assessment. Her talk will examine breakthrough technologies that are redefining education such as Generative AI (GenAI), neurotechnology, and brain-computer interfaces (BCIs) and consider how they might impact education in the coming years. Dr. Eaton will ground the rapid technological changes transforming education in the timeless principles of integrity, ethics, equity, and human rights. Dr. Eaton will talk about how these enduring cornerstones provide a foundation of hope for navigating an era of unprecedented technological progress. At the heart of it all, Dr. Eaton inspire us to think about how we can prepare today’s students to be ethical leaders and citizens of tomorrow. Postplagiarism serves as a backdrop for Dr. Eaton's lecture, which is considered a once-in-a-career honour at the Werklund School of Education, University of Calgary.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".