Attitudes of Pakistani Undergraduate ESL Students toward Artificial Intelligence in Improving English Writing Skills
Bibliographic record
Abstract
English writing proficiency is vital for educational, professional and personal development. With the rapid growth of Artificial Intelligence (AI) in academia, understanding its impact on language learning is essential. Students' attitudes toward AI influence their motivation, engagement, and learning outcomes. This mixed-method research aimed to explore the attitudes of Pakistani English as a Second Language (ESL) undergraduates toward AI in improving writing skills. Perceived usefulness, ease of use, and behavioral intentions of ESL undergraduates toward AI are examined through the integration of Technology Acceptance Model (TAM) and Constructivist Learning Theory (CLT). This study also investigated the influence of motivation, engagement, and societal expectations on AI adoption for improving writing skills along with the constructivist learning strategies used by students. Quantitative data were collected from 215 students through a Google survey and semi-structured interviews were used to gather qualitative data from 10 students. Descriptive statistics and percentages were used to analyze quantitative data; however, qualitative data were analyzed through thematic analysis. The findings highlight that ESL undergraduates’ attitudes are strongly influenced by perceived usefulness and ease of use; however, attitudes and behavioral intentions received slightly lower scores. Furthermore, challenges related to over-dependency, ethical considerations, and subscription issues affect AI adoption. Motivation and societal expectations increase AI adoption; on the contrary, learner-centered and interactive approaches are needed to enhance engagement levels. Additionally, this study suggests the need for AI ethical guidelines, institutional support, and literacy training to fully benefit from AI tools in improving writing skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".