Liu, Z., Yin, S. X., & Chen, N. (2024). Optimizing Code-Switching in Conversational Tutoring Systems: A Pedagogical Framework and Evaluation. Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 500–515. https://doi.org/10.18653/v1/2024.sigdial-1.43
Abstract:
Large language models demonstrate remarkable proficiency in various tasks across multiple languages. However, their potential in code-switching remains underexplored, particularly in cultural and educational contexts. Code-switching or translanguaging plays a crucial role in bilingual education, facilitating comprehension and engagement among students with varied linguistic proficiencies. In this work, we present a pedagogy-inspired framework that introduces traditional classroom practices of code-switching to intelligent tutoring systems. Specifically, we develop fine-grained instructional strategies tailored to multilingual and educational needs. We conduct experiments involving both LLM-based evaluation and expert analysis to assess the effectiveness of translanguaging in tutoring dialogues. Our experimental results indicate that strategic code-switching can significantly enhance the learning experience. This work not only advances dialogic tutors in language learning, but also extends LLMs to better accommodate multilingual interaction.
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Funding Info:
This research / project is supported by the National Research Foundation, Singapore - AISG Programme
Grant Reference no. : AISG2-GC2022-005
This research is supported by the Agency for Science, Technology and Research under the AI4EDU Programme