User Adaptive Language Learning Chatbots with a Curriculum
This work addresses the need for curriculum-aligned language learning chatbots for middle school students learning English as a second language, representing an incremental improvement over existing chitchat systems.
The paper tackled the problem of educational dialog systems not aligning with school curricula by adapting lexically constrained decoding to include textbook vocabulary in generated utterances, resulting in improved student understanding of target words and increased interest in practicing English.
Along with the development of systems for natural language understanding and generation, dialog systems have been widely adopted for language learning and practicing. Many current educational dialog systems perform chitchat, where the generated content and vocabulary are not constrained. However, for learners in a school setting, practice through dialog is more effective if it aligns with students' curriculum and focuses on textbook vocabulary. Therefore, we adapt lexically constrained decoding to a dialog system, which urges the dialog system to include curriculum-aligned words and phrases in its generated utterances. We adopt a generative dialog system, BlenderBot3, as our backbone model and evaluate our curriculum-based dialog system with middle school students learning English as their second language. The constrained words and phrases are derived from their textbooks, suggested by their English teachers. The evaluation result demonstrates that the dialog system with curriculum infusion improves students' understanding of target words and increases their interest in practicing English.