Use of AI Tools in Navigating Reading Difficulties of Adult EFL Learners
Bibliographic record
Abstract
Reading is often a neglected skill in language pedagogy. Reading is a very significant skill since language learning depends a lot on reading skill, and moreover, adult learners’ progress in higher education depends a lot on their reading habits. Fast reading with full comprehension is the target of developing reading skill. However, sometimes learners, particularly English as a Foreign Language (EFL) learners, find it difficult to achieve this objective. That results in the learners either developing painfully slow reading habits in English or low reading comprehension, if they try to be fast readers. Research studies have explored the common reading difficulties of such learners. However, research is still lacking in finding effective solutions to overcome the reading difficulties of adult EFL learners. Advanced AI applications used as educational aids have raised hopes in this regard. The present mixed-methods research with a quasi-experimental design, conducted over a three-month period with Saudi undergraduate EFL students, examined whether the AI tool ‘Actively Learn’ is effective in helping adult learners navigate their reading difficulties resulting in enhancement in their reading comprehension. The targeted aspects of reading activities to be improved in their reading course were speed of reading English texts, global comprehension, and grasping meanings of new words from their context. The results obtained show a gradual improvement in learners’ reading skills as the monthly tests report significant differences in their reading achievement from test to test (Pretest vs. Test 1: t 5.164 p 0.0001; Test 1 vs. Test 2: t 6.967 p 0.000; Test 2 vs. Test 3: t 7.934 p 0.000; Pretest vs. Test 4: t 19.12 p 0.000). Findings of the study are very significant, as they lay the foundation for a strong belief in the capability of AI-powered tools to impact students’ reading habits positively. In Saudi Arabian contexts, the study paves the way for further research in this significant academic area and adds to the body of existing research literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".