Gamified AI for EFL: A Model to Support Engagement and Collaboration in Language Classrooms
Bibliographic record
Abstract
This study developed and validated an instructional model that integrates Artificial Intelligence (AI), gamification, collaboration, and engagement in English as a Foreign Language (EFL) learning. Using a three-phase consensus-based approach, Phase 1 synthesized theoretical foundations from Communicative Language Teaching (CLT), Second Language Acquisition (SLA), Self-Determination Theory, and Social Interdependence Theory into an Integration Matrix of inputs, processes, mediating factors, and outputs. Phase 2 constructed the AI-Driven Gamified Collaboration Model within an Input-Process-Output (IPO) framework, while Phase 3 evaluated the model through expert consensus. Three specialists first assessed content validity using the Item-Objective Congruence (IOC) method, with all items meeting the acceptance criterion (IOC ≥ 0.67; consensus ≥ 80%). A separate panel of five experts then evaluated the model’s suitability on a 5-point Likert scale. The overall appropriateness of core model components was rated “Most Appropriate” (x̄ = 4.53, S.D. = 0.58), with the highest ratings for AI and technology integration (x̄ = 4.73) and theoretical alignment with CLT and SLA (x̄ = 4.67). Expert consensus confirmed the model’s validity and pedagogical relevance, providing a practical framework for fostering communicative competence, engagement, confidence, and learner autonomy in technology-enhanced EFL instruction.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".