Digital Deceit: Fake News, Artificial Intelligence, and Censorship in Educational Research
Bibliographic record
Abstract
Never has it been more urgent for educators to be aware of the perils of research in education using digital searches in today’s world of disinformation, misinformation, artificial intelligence and censorship. As a result, we are more reliant on strong researchers than ever before. In the discipline of Education, students are often asked to research issues pertaining to curricula, pedagogy, educational information and theories. Pupils are using Internet and digital library searches to gain knowledge within public and private K-12 schools and within higher education. In this article, an Educational Librarian and an Education Professor outline their approach to educating all Faculty of Education students about using digital platforms in relation to unmasking fake news, artificial intelligence (AI) usage, and increasing Internet censorship. Using case study research, we examined 34 Bachelor of Education students in training at the high school level who created environmental digital art projects. Information/media literacy was taught in order to provide students with the necessary tools to identify credible, diverse, well-informed, strong, and robust research. In addition, they needed to be able to discern when artificial intelligence was utilized. Outlined are students’ projects. Our findings include “top ten” practical suggestions for educators at all levels when teaching students about effective researching in our current digital era.
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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 | 0.056 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".