English Language Learners’ Perspectives On Implementing Gpts For Learning Purposes In The Context Of Ai Literacy-focused Pedagogical And Assessment Approach
Bibliographic record
Abstract
As AI-powered chatbots become prevalent in academia, there is a need for research practitioners working with English language learners (ELLs) to examine how students use generative AI (GenAI) for learning purposes. Teachers and researchers express concern with the potential detrimental impact of GenAI on ELLs’ intellectual engagement with learning (e.g., Barrot, 2023; Cardon et al., 2023; Cong-Lem et al., 2024). Meanwhile, emerging research highlights that for language educators, it is essential to evolve with technological advancements and - instead of attempting to avoid the unavoidable use of GenAI - apply such tools to promote sustained learning (Bui & Tong, 2025; Warschauer et al., 2023; Yeo, 2023).This article reports on a study in an English for Academic Purposes (EAP) course that incorporated ChatGPT into scaffolded assessment practices. The study explored students’ experiences using ChatGPT to brainstorm and revise their writing while immersed in AI literacy-focused instruction. Its post-instruction survey findings demonstrate that although students perceive GenAI as a tool to adapt to their learning needs, thereby enhancing language skills, they also recognize GenAI limitations (e.g., inaccurate output) and share concerns that are common among educators (e.g., overdependency on AI tools and breaching academic integrity). The implications suggest that when used responsibly, GenAI can supplement rather than replace students’ work. Educators might benefit from adopting a pedagogical and assessment framework focused on critical thinking, creativity, and digital literacy skills. Such approach fosters active learning through scaffolded formative assessments targeted at deconstructing, evaluating, and consciously incorporating GenAI-generated text into one’s writing.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".