Concept Borrowing or Theory Reconstructing? A Bibliometric Analysis of the Impact of AI on Foreign Language Teaching Using MANOVA and PCA Biplot

The rapid development of artificial intelligence (AI) is currently engendering profound changes across multiple disciplinary domains, and foreign language teaching (FLT) is no exception. Previous research on the status quo of the integration between AI and FLT has focused predominantly on bibliometric indicators such as cocitation networks and research hotspots but has failed to depict the essential characteristics of the current integration trend. Therefore, in this research, multivariate statistical analyses (i.e., MANOVA and PCA biplot) is conducted to illustrate the relationships among keyword frequency, betweenness centrality, publication year, and disciplinary category on the basis of the results of the bibliometric analysis. The research suggests a progressive “degeneration–centralization–integration” relationship in the AI and FLT integration process. The MANOVA and PCA biplot results further indicate that within the current hot topics of AI-assisted FLT research, most relevant studies remain at the stage of borrowing concepts from AI, and only a minority attempt to reconstruct FLT theories through AI technology.