Human-Centered Approaches to Promoting Democratic Values in EFL Classrooms
Bibliographic record
Abstract
The recent advancement of classroom practices after COVID-19 with digital communication, including AI chatbots, has sparked a renewed interest in exploring humanistic elements within classrooms. While the topic of AI generative chat is relatively new (launched in Nov 2022), seminal literature needs to be established. However, concerns have emerged about these technologies' potential consequences on authentic human communication. This review emphasizes the importance of maintaining human connections in educational settings, highlighting core democratic values guiding our classroom practices. An in-depth systematic mapping of literature defined by Creswell (2014) exploratory literature review guidelines was employed here to find available literature on major recurring themes and their interconnectedness through systematic mapping. It identifies the educational democratic philosophy founded by John Dewey, arguing for prioritizing creating and preserving the human-to-human interaction in the EFL classroom. Findings urged the need for classroom interventions to accommodate technological advances framed with democratic precepts and humanistic upbringing so that EFL classrooms remain exciting spaces for language learning, democratic engagement, and active participation.
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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 | 0.029 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".