Exploring the Teacher–Student–AI Triad in College EFL Teaching: A Perspective of HITL Theory
Bibliographic record
Abstract
The rapid rise of generative artificial intelligence (GenAI) presents both opportunities and challenges for English as a Foreign Language (EFL) education. While AI can support efficiency and personalization, concerns about student over-reliance, academic integrity, and diminished critical thinking remain pressing. This study explores these issues through the lens of Human-in-the-Loop (HITL) theory, which emphasizes the centrality of human oversight and intervention in automated systems. Building on HITL, the study develops a three-level framework for the teacher–student–AI triad: basic assistance, where AI functions as a background tool; collaborative innovation, where students engage with AI under teacher guidance; and reflective optimization, where AI evolves into a co-teacher through iterative feedback. To operationalize the framework, three case scenarios are designed for college EFL contexts: argumentative writing, impromptu speaking, and exploratory learning in literature. The study contributes theoretically by extending HITL into language education, providing a structured model for balancing AI assistance with human agency. Practically, the proposed scenarios offer educators concrete strategies to integrate AI in ways that are designed to enhance efficiency, creativity, and engagement while safeguarding academic rigor. The findings underscore the need to view AI not as a replacement but as a partner—one that enriches EFL pedagogy when guided by human-centered design.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".