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Enhancing Formal Writing Skills of Omani Post-Foundation EFL Students through QuillBot (an AI Tool) Integration

Aug 2026 · International Journal of Learning, Teaching and Educational Research · 0 citations · 37 references

Abstract

Artificial intelligence (AI) is being integrated into English as a foreign language (EFL) writing instruction, yet empirical evidence regarding its effectiveness in improving learners’ writing performance remains inconclusive, particularly in under-researched EFL contexts such as Oman. This study investigated the impact of AI-assisted instruction on formal writing proficiency among Omani post-foundation EFL students (N = 58) using a quasi-experimental pre-test–post-test mixed-methods research design. The intervention integrated QuillBot as a supplementary paraphrasing and grammar feedback tool within structured, teacher-guided writing tasks. A one-way ANCOVA, controlling for pre-test performance, revealed no statistically significant difference, as the AI?assisted intervention did not produce a statistically significant improvement in total formal writing scores or in any individual writing category relative to conventional instruction. Furthermore, the raw post-test scores in several categories, including Organization, favored the control group rather than the experimental group. Qualitative findings obtained through semi-structured interviews with the five most active students revealed broadly positive perceptions of the intervention, including increased confidence, reduced anxiety, and reported gains in error awareness and revision practices. However, this qualitative pattern did not consistently align with the quantitative findings. Overall, the findings indicate that short-term AI-assisted instruction using a single corrective tool did not yield measurable quantitative gains in formal writing performance within the study’s timeframe, even though some students reported a positive subjective experience with the tool. Thus, this study recommends the need for AI-tool integration for long-term interventions using larger samples, focusing on the measurement of student engagement with AI feedback.

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