A semi-systematic review of research on generative artificial intelligence (GenAI) in second-language acquisition (SLA)
Bibliographic record
Abstract
The release of ChatGPT in November 2022 led to a surge in research in Artificial Intelligence in Education (AIED), revealing new opportunities in education. In language learning, generative text models offer valuable affordances, such as supporting writing and reading comprehension. However, concerns related to academic integrity remain. As we approach the two-year milestone since the release of ChatGPT, the scientific community is immersed in an influx of publications in this rapidly evolving field. This necessitates an examination of the early state of research regarding the pedagogical implementation of GenAI in language learning. The semi-systematic review presented in this paper analyzes 12 primary studies of GenAI in language learning. The aim is to unveil overarching trends in the early research related to (1) participant and study characteristics and (2) key research themes. The results of this semi-systematic review revealed distinct trends. Two primary themes emerged: investigating learning and assessment Affordances and examining learner and teacher Perceptions. The implications of this semi-systematic review for future research will also be explored. Thus, this review provides valuable insights into the current state of research regarding GenAI’s role in language learning, paving the way for future investigations.
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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 | 0.026 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".