Re-embracing orality in digital education: the pedagogical affordances of podcasting in the era of generative AI
Bibliographic record
Abstract
In the evolving landscape of teacher education, where generative AI poses both opportunities and challenges, this article investigates the resurgence of orality through podcasting as a pedagogical tool. Situated within a teacher education program, the study focuses on teacher candidates who are navigating the complexities of educational technologies. It examines the role of student created podcasts for enhancing learning experiences, fostering collaborative communities, and developing essential teaching skills. Drawing on Walter Ong’s theory of orality and literacy, this phenomenological research explores how a digital return to orality can effectively counterbalance the impersonal nature of AI-generated content in education. The study argues that podcasting embodies human craftsmanship, revitalizes oral traditions in learning, and equips future educators with innovative pedagogical strategies in an increasingly digital academic environment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".