Integration of an AI-Based Platform and Flipped Classroom Instructional Model

This article aims to evaluate a flipped class teaching model based on an AI (artificial intelligence) Language Learning Platform. The platform adopts mainstream collaborative filtering and BPMF (Bayesian Probabilistic Matrix Factorization) and big data management methods to provide students’ detailed performance analysis reports and push recommendation exercises. The researcher reconciled the flipped teaching model with the AI-based language learning platforming in an experimental class and conventionally in a control class. At the end of the semester, the researcher compared students’ performance as well as their attitudes toward the course between the two classes through a postexam paper and a questionnaire. Combining quantitative and qualitative methods, the survey shows that although the AI-based language learning mode failed to significantly increase students’ English listening scores, compared with the students in the traditional class, those receiving the new mode held a more positive attitude toward their English listening experience, especially in regard to learning interest, study autonomy, and class involvement. The flipped class mode not only relieved students’ partial cognitive processing workloads in class, but also boosted their confidence to engage in classroom discussion by reducing their nervousness through before-class preview. The AI-based language learning platform mainly performs monitoring and tutoring functions which facilitate the flipped class instruction. This study proposes a new flipped teaching model and proves it effective, indicating that for the flipped class learning mode to be successful, it is of importance to integrate face-to-face classroom instruction with the AI-technology-assisted online learning experience in a way that they can coherently support each other.