Leading global excellence in pedagogy: augmenting teaching excellence: embracing the future of education with AI and emerging technologies
Bibliographic record
Abstract
The papers selected for this book showcases excellent teaching and learning practice that is provided by teachers nationally recognised for excellence in Universities in the UK, Canada, USA and Australia. With the growth of Artificial Intelligence and other emerging technologies in education the authors have thoughtfully shared innovative pedagogical practices of use in a variety of educational contexts and subject disciplines in higher education. This volume of Leading Global Excellence in Pedagogy provides a unique collection of papers which explores novel strategies for student engagement, assessment, and impactful teaching, and the ethical implications of AI in education. Featuring innovative practice using machine learning tools such as ChatGPT, and other cutting-edge technologies including Social Robotics. This book will help academics (teachers, researchers, and students) to consider the careful planning and design when implementing effective pedagogical practice, embracing AI transformative methodologies and other emerging technologies to augment teaching excellence and redefine teaching pedagogy. Both editors are National Teaching Fellows.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".