AI-driven CALL methodology: Amplifying minority language youth voices in digital citizenship education
Bibliographic record
Abstract
In a digital landscape increasingly marked by hate speech, disinformation, and radicalization (United Nations, 2022), fostering ethical and informed digital citizenship is a critical educational priority. The Living, Thinking, and Acting Online (Vivre, Réfléchir, Agir – #VRAenligne) project centered the voices of French minority-language youth in Canada from diverse linguistic and ethnic backgrounds. Structured in three phases, Living, Reflecting, and Acting, the project guided participants from exploring and articulating their own personal online experiences, to reflecting on what it means to be an ethical and digital citizen, and finally demonstrating a sense of agency and empowerment by committing to concrete actions promoting more inclusive digital spaces. Using a digital ethnography approach combined with AI-assisted multimodal artifact creation, the project engaged youth as co-researchers and co-creators of knowledge. Throughout each phase, participants used AI tools such as Canva and DALL·E to produce multimodal artifacts representing their identities and ethical perspectives. This creative process fostered active collaboration and co-reflection between youth and researchers, enabling a culturally and linguistically responsive exploration of lived experiences and digital citizenship. Findings demonstrate the strengths and possibilities of integrating generative AI into participatory research: it can amplify marginalized voices, deepen youth engagement, and support the development of digital literacy and civic agency. At the same time, the study highlights the ethical responsibility to navigate AI’s limitations, including risks of bias and misrepresentation, ensuring its use promotes socially responsible and equitable educational practices.
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 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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".