The Effectiveness of Using ELSA App on Improving Saudi Students’ English-Speaking Skills
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Bibliographic record
Abstract
Speaking is a challenging skill for the majority of English as a foreign language (EFL) learners around the world. Therefore, experts are researching the potential affordances of Artificial Intelligence (AI) as a tool to develop speaking skills. The ELSA App is a prominent example of an AI application that can provide instant corrections to the user to improve their speaking skills. Although app reviewers claim it is effective for enhancing speaking skills, there is minimal research confirming its effectiveness as a tool for enhancing key English-speaking skills, such as pronunciation, fluency, cohesion, and expanding lexical resources. To contribute to the literature in this area, this study sought to evaluate the efficacy of the ELSA App as a tool to develop Saudi high school students’ speaking skills. In addition, it aimed to explore their perceptions with regard to using the ELSA App to develop their English-speaking skills. In total, 30 high school students from Saudi Arabia were selected as the research sample. The participating students were divided into two groups, comprising 15 in the control group and 15 in the experimental group. A mixed-methods study design was used to answer the study questions. Data were collected using two research tools: a pre- and post-test speaking test, and a closed and open questionnaire. The students’ tests were corrected using IELTS rubrics. The questionnaire was arranged into two sections: closed questions based on a five-point Likert scale that was analyzed statistically, and open questions analyzed using content analysis. The results indicated that students in the experimental group experienced positive improvement at varying levels in terms of pronunciation, fluency, coherence, grammatical range and accuracy, and lexical resources in speaking skills. Moreover, the students had positive opinions about using the ELSA App to practice and develop their English-speaking skills. Thus, it appears the ELSA App in general had a significant positive impact on the Saudi student participants’ speaking skills, offering them a fertile interactive environment. This finding has valuable implications and prompted recommendations for teachers and EFL learners.
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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 | 0.004 | 0.180 |
| 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 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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