Examining EFL Students' Motivation Level in Using QuillBot to Improve Paraphrasing Skills
Bibliographic record
Abstract
Paraphrasing, being an essential component of academic writing skills, poses a challenge for EFL students. It requires motivation through integration of technology and artificial intelligence-mediated tool like QuillBot to address the issue. QuillBot, the online artificial intelligence tool, has the potential to assist and motivate students to improve their paraphrasing skills. This study, to address the scarcity of the available literature especially in Najran University context, aims to examine EFL students' motivation using QuillBot to improve their paraphrasing skills. To achieve the study objectives, the descriptive-diagnostic research design was followed. One hundred two students registered in Technical Writing course were the participants to respond to a questionnaire and semi-structured interview questions. The study explores whether there is any significant difference in the participants’ responses in terms of their gender. The results revealed that QuillBot highly motivated students to improve their paraphrasing skills from their point of view. Also, it was shown that gender influenced the respondents' answers in favor of females. Additionally, the content analysis showed that technology-mediated classrooms, personal digital gadgets, easy access to software and internet applications, proper guidance (how to use the AI tool to solve the paraphrasing exercises of the syllabus) to use AI etc. are factors that highly motivate EFL students to utilize QuillBot in improving their paraphrasing skills. The potential implications of these resources are to make writing classes more enjoyable, engaging, interactive, productive, and lively for students. Based on the findings, the study suggests EFL teachers use QuillBot to enhance paraphrasing skills, inspire students, adapt teaching methods to technology, while future research is recommended to explore essay and summary writing.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".