SVM-Based Analysis of AI Language Learning: Insights for Future Optimization
Lei Han · 2025
Language learning is a comprehensive learning process, it is not enough for us to learn speaking, grammar, text writing and so on. In the process of foreign language teaching in the past, the teacher's knowledge, experience, pronunciation and teaching methods directly affect the effect of teaching. However, the effectiveness of these foreign language teaching platform remains a challenge, because traditional evaluation methods usually lack effective insights into learning strategies. In this paper, we evaluate the impact of AI-based language learning platforms by integrating survey-based data collection and analyze it with machine learning. The survey was conducted to gather data on user demographics, study habits, AI platform usage, and self-reported learning progress. To provide a more accurate evaluation, we utilize Support Vector Machine (SVM) algorithms to analyze the survey results, predict user satisfaction, and evaluate the effectiveness of learning results. With the help of SVM algorithms, we provide several key factors which could influence the quality of language acquisition and provide insights for optimizing future foreign language teaching platforms. The results could provide a deeper understanding of AI-assisted language learning and emphasize the benefits of machine learning in improving personalized education.