EVALUATING THE USAGE AND IMPACT OF ARTIFICIAL INTELLIGENCE FOR REAL-TIME FEEDBACK ON ENGLISH LANGUAGE TASKS AND STUDENT’S PERFORMANCE IN COLLEGES OF EDUCATION

Abstract

This study assessed the effectiveness of AI-driven real-time feedback on students' performance in English language tasks at the Federal College of Education (Technical) Akoka. Specifically, the study evaluated the impact of such feedback on academic performance and to investigate students' perceptions, motivation, and engagement levels. Four research questions guided this inquiry, focusing on performance improvements, student perceptions of feedback, the effect on motivation and self-efficacy, and the relationship between feedback frequency and engagement. Employing a quasi-experimental research design, the population included 500 second-year students enrolled in English language courses. A purposive and stratified random sampling technique yielded a sample of 238 students for the Experimental Group (receiving AI feedback) and 262 for the Comparison Group (receiving traditional feedback). Data collection involved a Language Proficiency Test and a structured questionnaire, both of which demonstrated high validity and reliability, with Cronbach's Alpha coefficients of 0.86 and 0.91, respectively. Data were analyzed using descriptive statistics and inferential methods, including Independent Sample T-Test and correlation analysis. The findings revealed significant performance improvements in the Experimental Group, alongside positive student perceptions of real-time feedback. The conclusions indicate that AI feedback enhances language learning experiences and engagement levels. Major recommendations include the integration of AI feedback systems into the curriculum, training for educators on these technologies, and advocating for consistent feedback practices to bolster student engagement and performance.