Exploring the Impact of ChatGPT on Psychological Factors in Learning English Writing among Undergraduate Students
Bibliographic record
Abstract
The integration of artificial intelligence (AI) in education raises questions about its psychological impact on language learning, particularly for English writing. Previous studies have shown that ChatGPT positively affects learners' writing, but few studies have explored how ChatGPT impacts psychological factors in learning English writing.To address the gap,this exploratory study aimed to assess how ChatGPT influences psychological factors (cognition, emotions, motivation, attitudes and beliefs, psychological resilience, stress, and coping mechanisms) in learning English writing.The study employed a random sampling method to collect data from 142 undergraduate students at the University of Saudi Arabia. The study used a questionnaire to assess the psychological factors influencing learning in English writing.The analysis showed that ChatGPT significantly correlates with enhanced cognitive skills, including thought organization, vocabulary retention, and analytical abilities in English writing. Additionally, it increases motivation and engagement in writing tasks, supports adaptation to new writing challenges, and aids in managing writing-related stress. However, its effectiveness in reducing writing-related anxiety is inconsistent, suggesting the need for personalized strategies to address varied emotional responses in learning to write.This study highlights the need for customized educational strategies using AI tools like ChatGPT to enhance cognitive, motivational, and resilience factors while addressing varied emotional responses among English writing learners.
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 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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".