ChatGPT Integration in Writing Development: Student Experiences and Perspectives
Bibliographic record
Abstract
This study examines student perceptions of ChatGPT’s effectiveness for writing tasks within an English General Paper course at a pre-university institution in Brunei Darussalam. As the educational sector in Brunei Darussalam continues to evolve, there is an increasing emphasis on integrating digital technologies into teaching practices to enhance learning outcomes. Thirty-seven students utilized ChatGPT to assist them with various writing tasks, engaging in a collaborative process that encouraged exploration and creativity. The primary objective of this exercise was to evaluate how students integrated ChatGPT into their writing processes and assess its impact on their writing outcomes. A comprehensive survey was administered to gather qualitative data on student experiences, and perceived improvements in writing skills. Results indicated positive feedback from students on improving their writing skills, citing benefits such as saving time with the brainstorming of ideas, writing prompts on various topics, instant feedback on their drafts, and serving as a conversational partner for language learning and fostering critical thinking skills. However, students encountered difficulties with irrelevant and inaccurate responses from ChatGPT, and its limited creativity and originality. Some students expressed concerns about becoming overly reliant on the technology, which could potentially hinder their independent writing development. Consequently, this research offers practical insights and recommendations for educators seeking to implement new online learning and teaching tools in similar educational settings. It highlights the importance of considering the benefits and limitations of ChatGPT in enhancing English writing skills, emphasizing the need for guided integration into curricula. The findings inform future approaches to technology integration in language education, advocating for a balanced strategy that combines technological support with traditional writing instruction to maximize student learning outcomes.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".