Indigenous Pedagogies and the Implications of EdTech, Data, and AI in the Classroom
Bibliographic record
Abstract
The increased integration of educational technologies (EdTech) in recent history, combined with modern wireless connectivity and cloud potential, has led to a surge in data being extraction from the educators and learners who use them. Consequently, scientific innovations in artificial intelligence (AI) systems trained by large volumes of data are changing the educational landscape. Naturally, pedagogical approaches to education are evolving in line with philosophical, ideological, and theoretical understandings of technology, its uses, and its applications. This chapter explores the complex and evolving role of EdTech, its design, development, and deployment through Indigenous Pedagogies. In it, the authors weave through discussions that embrace Indigenous epistemologies, worldviews, cultures, and traditions. At the same time, they consider the transformative potential of embedding Indigenous Pedagogies into the ways we think about technology, beyond present understandings of EdTech. In doing so, the authors generate a discussion that aims to empower, educate, and lead to meaningful EdTech action.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".