BIPED: Pedagogically Informed Tutoring System for ESL Education
Soonwoo Kwon, Sojung Kim, Minju Park, Seunghyun Lee, Kyuseok Kim

TL;DR
This paper introduces BIPED, a dataset and framework for developing pedagogically informed conversational tutoring systems for ESL learners, leveraging GPT-4 and SOLAR-KO to enhance teaching of complex language concepts.
Contribution
The paper creates a novel bilingual pedagogically annotated dataset and proposes a two-step framework for training conversational tutors with diverse pedagogical strategies.
Findings
Models replicate human teaching styles
Models employ diverse pedagogical strategies
Models effectively handle complex language concepts
Abstract
Large Language Models (LLMs) have a great potential to serve as readily available and cost-efficient Conversational Intelligent Tutoring Systems (CITS) for teaching L2 learners of English. Existing CITS, however, are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learning strategies. To develop a more pedagogically informed CITS capable of teaching complex concepts, we construct a BIlingual PEDagogically-informed Tutoring Dataset (BIPED) of one-on-one, human-to-human English tutoring interactions. Through post-hoc analysis of the tutoring interactions, we come up with a lexicon of dialogue acts (34 tutor acts and 9 student acts), which we use to further annotate the collected dataset. Based on a two-step framework of first predicting the appropriate tutor act then generating the corresponding response, we implemented two CITS models…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning
MethodsAttention Is All You Need · Softmax · Layer Normalization · Linear Layer · Byte Pair Encoding · Label Smoothing · Adam · Residual Connection · Position-Wise Feed-Forward Layer · Multi-Head Attention
