Abstract:

Language education technology (LET) is one of the most representative and important components of smart education. In the face of new learning environments and technological trends, addressing and resolving the two fundamental contradictions of smart education—large-scale education versus personalized learning, and fragmented knowledge versus systematic construction—is particularly crucial in language education. Starting from the nature of language learning and the characteristics of contemporary artificial intelligence, this paper analyzes the limitations of existing online education models and language learning tools, and proposes a methodological framework that resolves the first contradiction through data-driven personalized learning services and the second through scenario-based learning and content-sharing mechanisms. It further elaborates a closed-loop mechanism for delivering high-quality data-driven learning services. Drawing on two concrete scenarios—machine language learning (i.e., programming) and international Chinese education—the paper introduces the practices of two online learning platforms, Shuishan Online and Shuishan Chinese, and analyzes the feasibility of this technical approach. Finally, the paper briefly discusses and prospects the technological development trends and practical issues of language education technology.

Key words: language education technologies, data-driven computational education, learning behavior data analysis, knowledge tracing, large language models