The Role of AI-Based Teaching and Assessment in Developing the Spoken Competence of ESP Learners
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The previous studies generally have been suffering from a lack of continuous oral production with personal feedback, and this paper investigates the contribution of AI-powered teaching and assessment to ESP learners' spoken competence While verbal interaction is essential for academic and career achievement, conventional teaching often does not provide the necessary immediacy and individualized nurturing required for perfecting the skill. The study employed a mixed-methods design, involving 40 ESP students who participated in pre-/post-tests, questionnaires, interviews, and machine-mediated oral activities. The integration of intelligent tools like automated speech recognition and real-time feedback systems were implemented over six weeks, helping learners keep close tabs on how they progressed in terms of fluency, accuracy, pronunciation and confidence. Results indicate significant oral proficiency gains that include greater fluency in speech production and increased ability to enunciate discipline-specific vocabulary. The non-judgmental and on-demand nature of AI practice also led to learners feeling less anxious and experiencing a greater sense of agency. While some technical issues persisted, teachers pointed out that immediate feedback facilitated quicker self-correction. In conclusion, study results indicate that while teacher-less AI can provide ESP oral training, it is the combination with teacher guidance that reinforces ESP oral training. The study highlights the importance of modern curriculum integration and proposes AI-augmented environments can forge more supportive, adaptive and learner-centred pathways to developing spoken competence. Long-term effects across a range of ESP fields should be investigated in future study.
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