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Artificial Intelligence Knowledge Development

<p style="text-align: justify;"><span style="font-size: 12pt; font-family: 'Cambria', serif; color: black;">This study examines the effectiveness of various feedback models in enhancing grammatical accuracy in English article usage among 140 Iranian EFL medical students. Using a randomized experimental design, participants were assigned to five groups: AI feedback alone (n=28), AI with immediate oral teacher feedback (n=30), AI with delayed written teacher feedback (n=35), a hybrid model combining AI with both teacher feedback types (n=24), and a control group receiving traditional feedback (n=23). To evaluate performance, we administered pre-tests, post-tests, and delayed post-tests, while a post-study survey gauged learner attitudes. The data revealed that the hybrid model yielded the most substantial improvements in accuracy and long-term retention (post-test M=21.83; delayed post-test M=21.29). The next most effective condition was AI coupled with immediate oral feedback. Notably, AI feedback used in isolation surpassed AI with delayed written feedback, underscoring the crucial role of immediacy in learning. Furthermore, the survey showed a strong positive correlation between learner attitudes and performance (r=.32, p&lt;.001), with the hybrid group expressing the most positive perceptions. These findings highlight the pedagogical value of integrating AI tools with timely, multi-modal teacher feedback to foster linguistic development and learner engagement in EFL contexts.</span></p>

ltrq.2026.54.05.pdf

Original Research 

ltrq.2026.54.05  .xml

Optimizing EFL Article Accuracy with Hybrid AI-Teacher Feedback