EduCSW: Building a Mandarin-English Code-Switched Generation Pipeline for Computer Science Learning
Year: 2025
Type: conference-paper
Abstract: This paper presents EduCSW, a novel pipeline for generating Mandarin-English codeswitched text to support AI-powered educational tools that adapt computer science instruction to learners' language proficiency through mixed-language delivery.To address the scarcity of code-mixed datasets, we propose an encoder-decoder architecture that generates natural code-switched text using only minimal existing code-mixed examples and parallel corpora.Evaluated on a corpus curated for computer science education, human annotators rated 60-64% of our model's outputs as natural, significantly outperforming both a baseline fine-tuned neural machine translation (NMT) model ( 22 -24%) and the DeepSeek-R1 model (34-44%).The generated text achieves a Code-Mixing Index (CMI) of 25.28%, aligning with patterns observed in spontaneous Mandarin-English code-switching.Designed to be generalizable across language pairs and domains, this pipeline lays the groundwork for generating training data to support the development of educational tools with dynamic code-switching capabilities.
Authors: Ruishi Chen, Yiling Zhao
Institution: Stanford University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412889187