AI, Audio, and Agriulture: Cross-Border Podcasting as a Tool for Digital Pedagogy and Sustainability Communication
Bibliographic record
Abstract
As digital platforms reshape agri-food systems, podcasts offer an accessible way to share sustainability solutions globally. In agricultural and natural resource education, podcasting aligns with project-based learning, allowing students to develop communication skills through content creation. Guided by the dialogic communication model and a project-based learning framework, this study explores how international digital collaborations can enrich agricultural and natural resource communication education. This qualitative case study examined the experiences of students, instructors, and Canadian agricultural and natural resource experts during a U.S.-based podcasting course that produced a podcast series on sustainability. Research questions addressed attitudes toward AI use in podcast production and experiences collaborating to produce the podcast. Data sources included instructor reflections, student podcasts, and collaborator surveys. Thematic analysis identified key insights in instructional design, intercultural communication, and knowledge exchange. Findings showed that digital dialogue and international collaboration supported global thinking and knowledge mobilization. Themes related to AI use included the value of intercultural dialogue, interconnectedness of agricultural and natural resource systems, and innovations in sustainability. Themes related to collaboration highlighted appreciation for real-world communication opportunities, though logistical challenges were noted. This study highlights podcasting as a tool for enhancing science communication and advisory services. Future courses should integrate AI tools for editing and dissemination while addressing ethical concerns around voice representation and misinformation. Further research should explore student autonomy and the evolving role of AI in educational content production
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".