A corpus-based approach to developing vocabulary curriculum materials for Indigenous youth: AI-generated versus human-created content
Bibliographic record
Abstract
Given inequitable access to learning resources caused by socioeconomic and historical issues, many Indigenous students have fallen behind grade expectations. This is in particular critical at the high school level, with increased academic English language demands in their learning of content areas. This paper reports on an exploratory study using corpus analysis that examined Indigenous students’ vocabulary use in verbal narratives, a spoken corpus—the representation of adolescent oral language competence at the school. It evaluated the feasibility, in terms of vocabulary coverages, of using both human-created and AI-generated narrative curriculum materials with enriched vocabulary to support culturally responsive vocabulary instruction. The study was conducted in an Indigenous high school in Canada, located in a remote First Nation community where most students spoke Ojibwe as their first language. The results found that Indigenous students’ narratives used a significant percentage of the first 1000 high frequency words and a small percentage of the second 1000 high frequency and academic words. Human-created and AI-generated narratives had a significantly higher percentage of the second 1000 high frequency words than Indigenous students’ narratives. Finally, AI-generated narratives contained a significantly higher percentage of academic words than both Indigenous youth’s and human-created narratives. The implications of the findings indicate that a data-driven corpus-based language pedagogy can be effective developing in innovative vocabulary instructional materials for future CALL interventions to support Indigenous youths. This can be achieved in light of culturally responsive pedagogy by leveraging Indigenous oral literacy traditions of storytelling, the youths’ narrative skills and their interests and aspirations. The present study has shown that some AI tools, along with carefully piloted prompts, have the capacity to efficiently co-develop vocabulary instructional content.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".