Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it