Challenges and Motivation: Assessing Gemini’s Impact on Undergraduate EFL Students in Classroom Settings
Bibliographic record
Abstract
The current study aimed to identify the challenges students encountered when using Gemini to learn English as a Foreign Language (EFL) and how successfully it motivates undergraduate students to learn the language in classroom settings. A pre-post quasi-experimental design was used, and data was gathered through an online questionnaire. One hundred fifty female EFL students participated. The results showed statistically significant differences, at the 0.05 significance level, between the study sample’s mean comments about how well Gemini as an AI tool motivates undergraduate students to learn EFL. The observed variations in the study significantly support the post-application stage. However, several challenges were identified in implementing Gemini in EFL, including repetitive words, limited vocabulary (62%), lengthy and non-concise answers (57.3%), uncertainty about information accuracy (49.3%), unclear question format comprehension (42%), and an abundance of similar information sources (39.3%). These findings call for further investigation to maximise Gemini’s potential and address both its promises and challenges, ultimately improving the EFL learning experience for all students.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".