Enhancing speaking skills through task repetition and ChatGPT integration in remedial EFL lessons: An action research approach
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Abstract
This study investigates the impact of integrating ChatGPT with repeated speaking tasks on remedial EFL learners' (N=17) speaking skills. The teacher in this study adopted evidence-based teaching through action research (Cochran-Smith & Lytle, 1990) by implementing a test-teach-test approach in a 7-week B1 course in a Turkish university. This involved integrating repeated speaking tasks to identify areas of grammar deficiency, followed by personalized support utilizing ChatGPT. The study employed a mixed-methods approach utilizing pre- and post-intervention data collection, such as questionnaires, speaking exams, and semi-structured interviews. Results indicate significant improvements in task completion and grammatical accuracy with post-test scores (task completion/content: M = 3.12, SD = 0.33; grammar: M = 3.00, SD = 0.00) significantly higher than pre-test scores (task completion/content: M = 2.29, SD = 0.47; grammar: M = 2.59, SD = 0.51). However, vocabulary and fluency did not show significant changes. Additionally, students' attitudes and motivation towards learning English improved significantly, with post-intervention levels (attitude: M = 3.29, SD = 0.47; motivation: M = 3.41, SD = 0.71) showing significant increases from pre-intervention (attitude: M = 2.41, SD = 0.51; motivation: M = 2.71, SD = 0.41). Combining ChatGPT for grammar use with task repetition significantly boosted students' motivation and speaking proficiency, as evidenced by improved task completion and language use. While vocabulary and fluency require further investigation, initial AI tool use benefits from close guidance. Key recommendations for educators and policymakers are outlined to optimize these benefits.
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