Improving the Learning of Language Proficiency at Tertiary Education Level Through AI-Driven Assessment Models and Automated Feedback Systems - Alshewiter, Kosay Moneer; Mohammad, Dr.fares(Saleh Sudqi)Ahmad; Shawaqfeh, Adawiya Taleb; Khasawneh, A. J.; Alqudah, Dr. Hatem; Khasawneh, Dr. Yusra jadallah abed; Khasawneh, Dr. Mohamad Ahmad Saleem (Migration Letters, 2023)
The impacts of artificial intelligence models in enhancing foreign language proficiency cannot be overemphasized. AI-driven assessment models and automated feedback systems are technological innovations that have enhanced the learning of different components of the foreign language. In this study, the focus was to revisit how AI-driven assessment models and automated feedback systems have helped foreign language learners achieve a high degree of proficiency in the foreign language. The study recruited a total of 529 undergraduates from Jordanian universities, who are studying different foreign languages to participate in the questionnaire survey that was designed using Google Forms. As a quantitative study, analysis was conducted using relevant statistical measures. Results from the analysis demonstrate nuanced views on automated feedback systems held by students. About 53.65% of participants recognized the benefit of automated feedback in giving timely and particular insights into their language ability, allowing them to effectively comprehend their strengths and areas for progress. However, 37.57% of students had reservations about the immediate form of feedback, which did not always give them the confidence to take part in language-learning activities without fear of making mistakes. The results also indicated that a combined 90.12% of participants accepted that receiving individualized feedback helped them set meaningful goals for their language learning and motivated them to achieve those goals. In addition, a whopping 86.58% of students used automated feedback systems to spot patterns of inaccurate language usage, demonstrating initiative in their pursuit of better grades. When participants had their accomplishments emphasized and their development over time highlighted, a combined 71.97% of them showed signs of motivation. All of these results together show how intricate the balance is between the advantages and disadvantages of automated feedback systems and AI-driven assessment models. Overall, the research found a mean score of 4.59 for motivation and academic success, suggesting that a personalized, dynamic, dialogue-driven feedback system that is sensitive to individual requirements might greatly improve the foreign language learning experiences of students.