Bibliographic record
Abstract
Artificial intelligence (AI) has the potential to transform language education by offering personalized instruction, real-time feedback, and tailored skill development. This study investigates how AI can refine attention to form in language teaching, enhancing the creation of language materials that increase the frequency and salience of L2 input, particularly in grammar instruction, aligning with principles of instructed second language acquisition. Understanding teachers' perceptions and preferences regarding AI is crucial, as it can significantly impact various aspects of language teaching in the near future. Using a qualitative research design, the study explores how nine pre-service teachers in Türkiye conceptualize attention to form and the role of AI technologies in form-focused instruction through semi-structured interviews and self-reflections. The findings indicate that participants used AI tools for generating input and crafting sentences and examples, and benefitted from well-contextualized examples with instant access to enriched input for grammar-focused lesson planning. L’intelligence artificielle (IA) a le potentiel de transformer l’enseignement des langues en offrant un enseignement personnalisé, une rétroaction en temps réel et un développement des compétences sur mesure. Cette étude examine comment l’IA peut favoriser l’attention portée à la forme dans l’enseignement des langues, en améliorant la création de matériel pédagogique qui augmente la fréquence et la mise en évidence de certains éléments de l’intrant en langue seconde. Ceci s’applique en particulier sur l’enseignement de la grammaire, en cohérence avec les principes de l’acquisition d’une langue seconde appliquée à l’enseignement. Il est essentiel de comprendre les perceptions et les préférences des enseignants à l’égard de l’IA, car celle-ci pourrait avoir un impact significatif sur divers aspects de l’enseignement des langues dans un avenir proche. En adoptant un devis de recherche qualitative, l’étude explore la façon dont neuf enseignants en formation en Turquie conceptualisent l’attention portée à la forme et le rôle des technologies d’IA dans l’enseignement de la forme, par le biais d’entretiens semidirigés et d’autoréflexions. Les résultats indiquent que les participants ont utilisé des outils d’IA pour générer de l’intrant et élaborer des phrases et des exemples, et qu’ils ont bénéficié d’exemples bien contextualisés avec un accès instantané à un intrant enrichi pour planifier des leçons axées sur la grammaire.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".