Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions

This paper investigates how pedagogical instructions facilitate scaffolding in LLM-based intelligent tutoring systems through a case study of guiding children to describe images for language learning. The authors construct different tutoring systems grounded in four learning theories and develop a seven-dimension rubric to evaluate the scaffolding process, including both manual and automated evaluation approaches.

Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education. With the emergence of generative artificial intelligence, large language models (LLMs) further entitle the systems to complex and coherent conversational interactions. These systems would be of great help in language education as it involves developing skills in communication, which, however, drew relatively le

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