Speaking to write: examining language learners’ acceptance of automatic speech recognition as a writing tool
Bibliographic record
Abstract
This mixed-methods one-shot study examines L2 writers’ perceptions of using Automatic Speech Recognition (ASR) to write using the Technology Acceptance Model (TAM), based on three criteria: usefulness, ease of use, and intention to use. After receiving training on Google voice typing in Google Docs, 17 English as a Second Language (ESL) students carried out two ASR-based writing tasks over a two-hour period. After the treatment, participants filled in a TAM-informed survey and participated in semi-structured interviews to measure their perceptions based on the target criteria. Findings indicate positive perceptions of ASR as a writing tool in terms of usefulness (language learning potential) and ease of use (e.g. user-friendly voice commands). We believe that these positive perceptions might lead to an intention to continue to use ASR, suggesting that the technology has L2 pedagogical potential.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".