Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners
Bibliographic record
Abstract
Abstract ChatGPT, an artificial intelligence application, has emerged as a promising educational tool with a wide range of applications, attracting the attention of researchers and educators. This qualitative case study, chosen for its ability to provide an in-depth exploration of the nuanced effects of AI on the foreign language learning process within its real-world educational context, aimed to utilize ChatGPT in foreign language education, addressing a gap in existing research by offering insights into the potential, benefits, and drawbacks of this innovative approach. The study involved 13 preparatory class students studying at the School of Foreign Languages at a university in Turkey. The students were introduced to ChatGPT through learning experiences over a span of four weeks by the researcher as a language teacher. The qualitative data collected from the interviews were analysed using thematic analysis. The findings suggest that ChatGPT positively affects students’ learning experiences, especially in writing, grammar, and vocabulary acquisition, and enhances motivation and engagement through its versatile and accessible nature in various learning activities. These insights contribute to understanding the utility and constraints of employing ChatGPT technology in foreign language instruction and can inform educators and researchers in developing effective teaching strategies and in designing curricula.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".